Data Management & Statistics

For all its vast emptiness, the universe is humming with activity in the form of gravitational waves. Produced by extreme astrophysical phenomena, these reverberations ripple forth and shake the fabric of space-time, like the clang of a cosmic bell.
Now researchers have detected a signal from what may be the most massive black hole merger yet observed in gravitational waves. The product of the merger is the first clear detection of an “intermediate-mass” black hole, with a mass between 100 and 1,000 times that of the sun.
They detected the signal, which they have labeled GW190521, on May 21, 2019, with the National Science Foundation’s Laser Interferometer Gravitational-wave Observatory (LIGO), a pair of identical, 4-kilometer-long interferometers in the United States; and Virgo, a 3-kilometer-long detector in Italy.
The signal, resembling about four short wiggles, is extremely brief in duration, lasting less than one-tenth of a second. From what the researchers can tell, GW190521 was generated by a source that is roughly 5 gigaparsecs away, when the universe was about half its age, making it one of the most distant gravitational-wave sources detected so far.
As for what produced this signal, based on a powerful suite of state-of-the-art computational and modeling tools, scientists think that GW190521 was most likely generated by a binary black hole merger with unusual properties.
Almost every confirmed gravitational-wave signal to date has been from a binary merger, either between two black holes or two neutron stars. This newest merger appears to be the most massive yet, involving two inspiraling black holes with masses about 85 and 66 times the mass of the sun.
The LIGO-Virgo team has also measured each black hole’s spin and discovered that as the black holes were circling ever closer together, they could have been spinning about their own axes, at angles that were out of alignment with the axis of their orbit. The black holes’ misaligned spins likely caused their orbits to wobble, or “precess,” as the two Goliaths spiraled toward each other.
The new signal likely represents the instant that the two black holes merged. The merger created an even more massive black hole, of about 142 solar masses, and released an enormous amount of energy, equivalent to around 8 solar masses,      spread across the universe in the form of gravitational waves.
“This doesn’t look much like a chirp, which is what we typically detect,” says Virgo member Nelson Christensen, a researcher at the French National Centre for Scientific Research (CNRS), comparing the signal to LIGO’s first detection of gravitational waves in 2015. “This is more like something that goes ‘bang,’ and it’s the most massive signal LIGO and Virgo have seen.”
The international team of scientists, who make up the LIGO Scientific Collaboration (LSC) and the Virgo Collaboration, have reported their findings in two papers published today. One, appearing in Physical Review Letters, details the discovery, and the other, in The Astrophysical Journal Letters, discusses the signal’s physical properties and astrophysical implications.
“LIGO once again surprises us not just with the detection of black holes in sizes that are difficult to explain, but doing it using techniques that were not designed specifically for stellar mergers,” says Pedro Marronetti, program director for gravitational physics at the National Science Foundation. “This is of tremendous importance since it showcases the instrument’s ability to detect signals from completely unforeseen astrophysical events. LIGO shows that it can also observe the unexpected.”

In the mass gap
The uniquely large masses of the two inspiraling black holes, as well as the final black hole, raise a slew of questions regarding their formation.
All of the black holes observed to date fit within either of two categories: stellar-mass black holes, which measure from a few solar masses up to tens of solar masses and are thought to form when massive stars die; or supermassive black holes, such as the one at the center of the Milky Way galaxy, that are from hundreds of thousands, to billions of times that of our sun.
However, the final 142-solar-mass black hole produced by the GW190521 merger lies within an intermediate mass range between stellar-mass and supermassive black holes — the first of its kind ever detected.
The two progenitor black holes that produced the final black hole also seem to be unique in their size. They’re so massive that scientists suspect one or both of them may not have formed from a collapsing star, as most stellar-mass black holes do.
According to the physics of stellar evolution, outward pressure from the photons and gas in a star’s core support it against the force of gravity pushing inward, so that the star is stable, like the sun. After the core of a massive star fuses nuclei as heavy as iron, it can no longer produce enough pressure to support the outer layers. When this outward pressure is less than gravity, the star collapses under its own weight, in an explosion called a core-collapse supernova, that can leave behind a black hole.
This process can explain how stars as massive as 130 solar masses can produce black holes that are up to 65 solar masses. But for heavier stars, a phenomenon known as “pair instability” is thought to kick in. When the core’s photons become extremely energetic, they can morph into an electron and antielectron pair. These pairs generate less pressure than photons, causing the star to become unstable against gravitational collapse, and the resulting explosion is strong enough to leave nothing behind. Even more massive stars, above 200 solar masses, would      eventually collapse directly into a black hole of at least 120 solar masses. A collapsing star, then, should not be able to produce a black hole between approximately 65 and 120 solar masses — a range that is known as the “pair instability mass gap.”
But now, the heavier of the two black holes that produced the GW190521 signal, at 85 solar masses, is the first so far detected within the pair instability mass gap.
“The fact that we’re seeing a black hole in this mass gap will make a lot of astrophysicists scratch their heads and try to figure out how these black holes were made,” says Christensen, who is the director of the Artemis Laboratory at the Nice Observatory in France.
One possibility, which the researchers consider in their second paper, is of a hierarchical merger, in which the two progenitor black holes themselves may have formed from the merging of two smaller black holes, before migrating together and eventually merging.
“This event opens more questions than it provides answers,” says LIGO member Alan Weinstein, professor of physics at Caltech. “From the perspective of discovery and physics, it’s a very exciting thing.”
“Something unexpected”
There are many remaining questions regarding GW190521.
As LIGO and Virgo detectors listen for gravitational waves passing through Earth, automated searches comb through the incoming data for interesting signals. These searches can use two different methods: algorithms that pick out specific wave patterns in the data that may have been produced by compact binary systems; and more general “burst” searches, which essentially look for anything out of the ordinary.
LIGO member Salvatore Vitale, assistant professor of physics at MIT, likens compact binary searches to “passing a comb through data, that will catch things in a certain spacing,” in contrast to burst searches that are more of a “catch-all” approach.
In the case of GW190521, it was a burst search that picked up the signal slightly more clearly, opening the very small chance that the gravitational waves arose from something other than a binary merger.
“The bar for asserting we’ve discovered something new is very high,” Weinstein says. “So we typically apply Occam’s razor: The simpler solution is the better one, which in this case is a binary black hole.”
But what if something entirely new produced these gravitational waves? It’s a tantalizing prospect, and in their paper the scientists briefly consider other sources in the universe that might have produced the signal they detected. For instance, perhaps the gravitational waves were emitted by a collapsing star in our galaxy. The signal could also be from a cosmic string produced just after the universe inflated in its earliest moments — although neither of these exotic possibilities matches the data as well as a binary merger.
“Since we first turned on LIGO, everything we’ve observed with confidence has been a collision of black holes or neutron stars,” Weinstein says “This is the one event where our analysis allows the possibility that this event is not such a collision.  Although this event is consistent with being from an exceptionally massive binary black hole merger, and alternative explanations are disfavored, it is pushing the boundaries of our confidence. And that potentially makes it extremely exciting. Because we have all been hoping for something new, something unexpected, that could challenge what we’ve learned already. This event has the potential for doing that.”
This research was funded by the U.S. National Science Foundation. More

New faculty in these areas will connect the MIT Schwarzman College of Computing and a department or school. More

MIT News – Data | Big data | Analytics | Statistics | IDSS | Operations researchReal-time data for a better response to disease outbreaksThe factory of the future, batteries not includedData systems that learn to be betterMasks mandates have major impact, study findsAn automated health care system that understands when to step inThe tenured engineers of 2020Faculty receive funding to develop artificial intelligence techniques to combat Covid-19Learners today, leaders tomorrowWhat is the Covid-19 data tsunami telling policymakers?What is the Covid-19 data tsunami telling policymakers?What is the Covid-19 data tsunami telling policymakers?What is the Covid-19 data tsunami telling policymakers?Ali Jadbabaie named head of the Department of Civil and Environmental EngineeringImproving global health equity by helping clinics do more with lessImproving global health equity by helping clinics do more with lessBringing the predictive power of artificial intelligence to health careFrom delayed deceleration to ZoomingThe social life of dataIf transistors can’t get smaller, then coders have to get smarterAlgorithm quickly simulates a roll of loaded diceA data-driven response to a pandemicA data-driven response to a pandemicA data-driven response to a pandemicA data-driven response to a pandemicReporting tool aims to balance hospitals’ Covid-19 loadOptimizing complex decision-makingAnnual Women in Data Science conference discusses fake newsAnnual Women in Data Science conference discusses fake news3 Questions: Catherine D’Ignazio on visualizing Covid-19 dataReducing delays in wireless networksAccelerating data-driven discoveriesAccelerating data-driven discoveriesMIT’s entrepreneurial ecosystem steps up to the challenge of Covid-19The 2020 U.S. census: Time to make it countSupply chain outlook: The timing of the slowdown3Q: Collaborating with users to develop accessible designsEvents postponed or canceled as MIT responds to COVID-19Creating Peru’s next generation of data scientistsCreating Peru’s next generation of data scientistsCreating Peru’s next generation of data scientistsThe elephant in the server roomProtecting sensitive metadata so it can’t be used for surveillanceMIT continues to advance toward greenhouse gas reduction goalsA human-machine collaboration to defend against cyberattacksAutomated system can rewrite outdated sentences in Wikipedia articlesBrainstorming energy-saving hacks on Satori, MIT’s new supercomputerHey Alexa! Sorry I fooled you …A college for the computing ageA college for the computing ageMIT launches master’s in data, economics, and development policy, led by Nobel laureateshttps://news.mit.edu/rss/topic/data-management-and-statistics MIT news feed about: Data | Big data | Analytics | Statistics | IDSS | Operations research en Fri, 21 Aug 2020 00:00:00 -0400 https://news.mit.edu/2020/kinsa-health-0821 The startup Kinsa uses its smart thermometers to detect and track the spread of contagious illness before patients go to the hospital. Fri, 21 Aug 2020 00:00:00 -0400 https://news.mit.edu/2020/kinsa-health-0821 Zach Winn | MIT News Office Kinsa was founded by MIT alumnus Inder Singh MBA ’06, SM ’07 in 2012, with the mission of collecting information about when and where infectious diseases are spreading in real-time. Today the company is fulfilling that mission along several fronts. It starts with families. More than 1.5 million of Kinsa’s “smart” thermometers have been sold or given away across the country, including hundreds of thousands to families from low-income school districts. The thermometers link to an app that helps users decide if they should seek medical attention based on age, fever, and symptoms. At the community level, the data generated by the thermometers are anonymized and aggregated, and can be shared with parents and school officials, helping them understand what illnesses are going around and prevent the spread of disease in classrooms. By working with over 2,000 schools to date in addition to many businesses, Kinsa has also developed predictive models that can forecast flu seasons each year. In the spring of this year, the company showed it could predict flu spread 12-20 weeks in advance at the city level. The milestone prepared Kinsa for its most profound scale-up yet. When Covid-19 came to the U.S., the company was able to estimate its spread in real-time by tracking fever levels above what would normally be expected. Now Kinsa is working with health officials in five states and three cities to help contain and control the virus. “By the time the CDC [U.S. Centers for Disease Control] gets the data, it has been processed, deidentified, and people have entered the health system to see a doctor,” say Singh, who is Kinsa’s CEO as well as its founder. “There’s a huge delay from when someone contracts an illness and when they see a doctor. The current health care system only sees the latter; we see the former.” Today Kinsa finds itself playing a central role in America’s Covid-19 response. In addition to its local partnerships, the company has become a central information hub for the public, media, and researchers with its Healthweather tool, which maps unusual rates of fevers — among the most common symptom of Covid-19 — to help visualize the prevalence of illness in communities. Singh says Kinsa’s data complement other methods of containing the virus like testing, contact tracing, and the use of face masks. Better data for better responses Singh’s first exposure to MIT came while he was attending the Harvard University Kennedy School of Government as a graduate student. “I remember I interacted with some MIT undergrads, we brainstormed some social-impact ideas,” Singh recalls. “A week later I got an email from them saying they’d prototyped what we were talking about. I was like, ‘You prototyped what we talked about in a week!?’ I was blown away, and it was an insight into how MIT is such a do-er campus. It was so entrepreneurial. I was like, ‘I want to do that.’” Soon Singh enrolled in the Harvard-MIT Program in Health Sciences and Technology, an interdisciplinary program where Singh earned his master’s and MBA degrees while working with leading research hospitals in the area. The program also set him on a course to improve the way we respond to infectious disease. Following his graduation, he joined the Clinton Health Access Initiative (CHAI), where he brokered deals between pharmaceutical companies and low-resource countries to lower the cost of medicines for HIV, malaria, and tuberculosis. Singh described CHAI as a dream job, but it opened his eyes to several shortcomings in the global health system. “The world tries to curb the spread of infectious illness with almost zero real-time information about when and where disease is spreading,” Singh says. “The question I posed to start Kinsa was ‘how do you stop the next outbreak before it becomes an epidemic if you don’t know where and when it’s starting and how fast it’s spreading’?” Kinsa was started in 2012 with the insight that better data were needed to control infectious diseases. In order to get that data, the company needed a new way of providing value to sick people and families. “The behavior in the home when someone gets sick is to grab the thermometer,” Singh says. “We piggy-backed off of that to create a communication channel to the sick, to help them get better faster.” Kinsa started by selling its thermometers and creating a sponsorship program for corporate donors to fund thermometer donations to Title 1 schools, which serve high numbers of economically disadvantaged students. Singh says 40 percent of families that receive a Kinsa thermometer through that program did not previously have any thermometer in their house. The company says its program has been shown to help schools improve attendance, and has yielded years of real-time data on fever rates to help compare to official estimates and develop its models. “We had been forecasting flu incidence accurately several weeks out for years, and right around early 2020, we had a massive breakthrough,” Singh recalls. “We showed we could predict flu 12 to 20 weeks out — then March hit. We said, let’s try to remove the fever levels associated with cold and flu from our observed real time signal. What’s left over is unusual fevers, and we saw hotspots across the country. We observed six years of data and there’d been hot spots, but nothing like we were seeing in early March.” The company quickly made their real-time data available to the public, and on March 14, Singh got on a call with the former New York State health commissioner, the former head of the U.S. Food and Drug Administration, and the man responsible for Taiwan’s successful Covid-19 response. “I said, ‘There’s hotspots everywhere,” Singh recalls. “They’re in New York, around the Northeast, Texas, Michigan. They said, ‘This is interesting, but it doesn’t look credible because we’re not seeing case reports of Covid-19.’ Low and behold, days and weeks later, we saw the Covid cases start building up.” A tool against Covid-19 Singh says Kinsa’s data provide an unprecedented look into the way a disease is spreading through a community. “We can predict the entire incidence curve [of flu season] on a city-by-city basis,” Singh says. “The next best model is [about] three weeks out, at a multistate level. It’s not because we’re smarter than others; it’s because we have better data. We found a way to communicate with someone consistently when they’ve just fallen ill.” Kinsa has been working with health departments and research groups around the country to help them interpret the company’s data and react to early warnings of Covid-19’s spread. It’s also helping companies around the country as they begin bringing employees back to offices. Now Kinsa is working on expanding its international presence to help curb infectious diseases on multiple fronts around the world, just like it’s doing in the U.S. The company’s progress promises to help authorities monitor diseases long after Covid-19. “I started Kinsa to create a global, real-time outbreak monitoring and detection system, and now we have predictive power beyond that,” Singh says. “When you know where and when symptoms are starting and how fast their spreading, you can empower local individuals, families, communities, and governments.” The startup Kinsa, founded by MIT alumnus Inder Singh MBA ’06, SM ’07, uses data generated by its thermometers to detect and track contagious illness earlier than methods that rely on hospital testing. Image: Courtesy of Kinsa https://news.mit.edu/2020/everactive-sensors-0820 Everactive provides an industrial “internet of things” platform built on its battery-free sensors. Thu, 20 Aug 2020 00:00:00 -0400 https://news.mit.edu/2020/everactive-sensors-0820 Zach Winn | MIT News Office Many analysts have predicted an explosion in the number of industrial “internet of things” (IoT) devices that will come online over the next decade. Sensors play a big role in those forecasts. Unfortunately, sensors come with their own drawbacks, many of which are due to the limited energy supply and finite lifetime of their batteries. Now the startup Everactive has developed industrial sensors that run around the clock, require minimal maintenance, and can last over 20 years. The company created the sensors not by redesigning its batteries, but by eliminating them altogether. The key is Everactive’s ultra-low-power integrated circuits, which harvest energy from sources like indoor light and vibrations to generate data. The sensors continuously send that data to Everactive’s cloud-based dashboard, which gives users real time insights, analysis, and alerts to help them leverage the full power of industrial IoT devices. “It’s all enabled by the ultra-low-power chips that support continuous monitoring,” says Everactive Co-Chief Technology Officer David Wentzloff SM ’02, PhD ’07. “Because our source of power is unlimited, we’re not making tradeoffs like keeping radios off or doing something else [limiting] to save battery life.” Everactive builds finished products on top of its chips that customers can quickly deploy in large numbers. Its first product monitors steam traps, which release condensate out of steam systems. Such systems are used in a variety of industries, and Everactive’s customers include companies in sectors like oil and gas, paper, and food production. Everactive has also developed a sensor to monitor rotating machinery, like motors and pumps, that runs on the second generation of its battery-free chips. By avoiding the costs and restrictions associated with other sensors, the company believes it’s well-positioned to play a role in the IoT-powered transition to the factory of the future. “This is technology that’s totally maintenance free, with no batteries, powered by harvested energy, and always connected to the cloud. There’s so many things you can do with that, it’s hard to wrap your head around,” Wentzloff says. Breaking free from batteries Wentzloff and his Everactive co-founder and co-CTO Benton Calhoun SM ’02, PhD ’06 have been working on low-power circuit design for more than a decade, beginning with their time at MIT. They both did their PhD work in the lab of Anantha Chandrakasan, who is currently the Vannevar Bush Professor of Electrical Engineering and Computer Science and the dean of MIT’s School of Engineering. Calhoun’s research focused on low-power digital circuits and memory while Wentzloff’s focused on low power radios. After earning their PhDs, both men became assistant professors at the schools they attended as undergraduates — Wentzloff at the University of Michigan and Calhoun at the University of Virginia — where they still teach today. Even after settling in different parts of the country, they continued collaborating, applying for joint grants and building circuit-based systems that combined their areas of research. The collaboration was not an isolated incident: The founders have maintained relationships with many of their contacts from MIT. “To this day I stay in touch with my colleagues and professors,” Wentzloff says. “It’s a great group to be associated with, especially when you talk about the integrated circuit space. It’s a great community, and I really value and appreciate that experience and those connections that have come out of it. That’s far and away the longest impression MIT has left on my career, those people I continue to stay in touch with. We’re all helping each other out.” Wentzloff and Calhoun’s academic labs eventually created a battery-free physiological monitor that could track a user’s movement, temperature, heart rate, and other signals and send that data to a phone, all while running on energy harvested from body heat. “That’s when we decided we should look at commercializing this technology,” Wentzloff says. In 2014, they partnered with semiconductor industry veteran Brendan Richardson to launch the company, originally called PsiKick. In the beginning, when Wentzloff describes the company as “three guys and a dog in a garage,” the founders sought to reimagine circuit designs that included features of full computing systems like sensor interfaces, processing power, memory, and radio signals. They also needed to incorporate energy harvesting mechanisms and power management capabilities. “We wiped the slate clean and had a fresh start,” Wentzloff recalls. The founders initially attempted to sell their chips to companies to build solutions on top of, but they quickly realized the industry wasn’t familiar enough with battery-free chips. “There’s an education level to it, because there’s a generation of engineers used to thinking of systems design with battery-operated chips,” Wentzloff says. The learning curve led the founders to start building their own solutions for customers. Today Everactive offers its sensors as part of a wider service that incorporates wireless networks and data analytics. The company’s sensors can be powered by small vibrations, lights inside a factory as dim as 100 lux, and heat differentials below 10 degrees Fahrenheit. The devices can sense temperature, acceleration, vibration, pressure, and more. The company says its sensors cost significantly less to operate than traditional sensors and avoid the maintenance headache that comes with deploying thousands of battery-powered devices. For instance, Everactive considered the cost of deploying 10,000 traditional sensors. Assuming a three-year battery life, the customer would need to replace an average of 3,333 batteries each year, which comes out to more than nine a day. The next technological revolution By saving on maintenance and replacement costs, Everactive customers are able to deploy more sensors. That, combined with the near-continuous operation of those sensors, brings a new level of visibility to operations. “[Removing restrictions on sensor installations] starts to give you a sixth sense, if you will, about how your overall operations are running,” Calhoun says. “That’s exciting. Customers would like to wave a magic wand and know exactly what’s going on wherever they’re interested. The ability to deploy tens of thousands of sensors gets you close to that magic wand.” With thousands of Everactive’s steam trap sensors already deployed, Wentzloff believes its sensors for motors and other rotating machinery will make an even bigger impact on the IoT market. Beyond Everactive’s second generation of products, the founders say their sensors are a few years away from being translucent, flexible, and the size of a postage stamp. At that point customers will simply need to stick the sensors onto machines to start generating data. Such ease of installation and use would have implications far beyond the factory floor. “You hear about smart transportation, smart agriculture, etc.,” Calhoun says. “IoT has this promise to make all of our environments smart, meaning there’s an awareness of what’s going on and use of that information to have these environments behave in ways that anticipate our needs and are as efficient as possible. We believe battery-less sensing is required and inevitable to bring about that vision, and we’re excited to be a part of that next computing revolution.” The startup Everactive uses ultra-low power chips to run its industrial “internet of things” platform on battery-less sensors. Image courtesy of Everactive https://news.mit.edu/2020/mit-data-systems-learn-be-better-tsunami-bao-0810 Storage tool developed at MIT CSAIL adapts to what its datasets’ users want to search. Mon, 10 Aug 2020 16:05:00 -0400 https://news.mit.edu/2020/mit-data-systems-learn-be-better-tsunami-bao-0810 Adam Conner-Simons | MIT CSAIL Big data has gotten really, really big: By 2025, all the world’s data will add up to an estimated 175 trillion gigabytes. For a visual, if you stored that amount of data on DVDs, it would stack up tall enough to circle the Earth 222 times.  One of the biggest challenges in computing is handling this onslaught of information while still being able to efficiently store and process it. A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that the answer rests with something called “instance-optimized systems.”   Traditional storage and database systems are designed to work for a wide range of applications because of how long it can take to build them — months or, often, several years. As a result, for any given workload such systems provide performance that is good, but usually not the best. Even worse, they sometimes require administrators to painstakingly tune the system by hand to provide even reasonable performance.  In contrast, the goal of instance-optimized systems is to build systems that optimize and partially re-organize themselves for the data they store and the workload they serve.  “It’s like building a database system for every application from scratch, which is not economically feasible with traditional system designs,” says MIT Professor Tim Kraska.  As a first step toward this vision, Kraska and colleagues developed Tsunami and Bao. Tsunami uses machine learning to automatically re-organize a dataset’s storage layout based on the types of queries that its users make. Tests show that it can run queries up to 10 times faster than state-of-the-art systems. What’s more, its datasets can be organized via a series of “learned indexes” that are up to 100 times smaller than the indexes used in traditional systems.  Kraska has been exploring the topic of learned indexes for several years, going back to his influential work with colleagues at Google in 2017.  Harvard University Professor Stratos Idreos, who was not involved in the Tsunami project, says that a unique advantage of learned indexes is their small size, which, in addition to space savings, brings substantial performance improvements. “I think this line of work is a paradigm shift that’s going to impact system design long-term,” says Idreos. “I expect approaches based on models will be one of the core components at the heart of a new wave of adaptive systems.” Bao, meanwhile, focuses on improving the efficiency of query optimization through machine learning. A query optimizer rewrites a high-level declarative query to a query plan, which can actually be executed over the data to compute the result to the query. However, often there exists more than one query plan to answer any query; picking the wrong one can cause a query to take days to compute the answer, rather than seconds.  Traditional query optimizers take years to build, are very hard to maintain, and, most importantly, do not learn from their mistakes. Bao is the first learning-based approach to query optimization that has been fully integrated into the popular database management system PostgreSQL. Lead author Ryan Marcus, a postdoc in Kraska’s group, says that Bao produces query plans that run up to 50 percent faster than those created by the PostgreSQL optimizer, meaning that it could help to significantly reduce the cost of cloud services, like Amazon’s Redshift, that are based on PostgreSQL. By fusing the two systems together, Kraska hopes to build the first instance-optimized database system that can provide the best possible performance for each individual application without any manual tuning.  The goal is to not only relieve developers from the daunting and laborious process of tuning database systems, but to also provide performance and cost benefits that are not possible with traditional systems. Traditionally, the systems we use to store data are limited to only a few storage options and, because of it, they cannot provide the best possible performance for a given application. What Tsunami can do is dynamically change the structure of the data storage based on the kinds of queries that it receives and create new ways to store data, which are not feasible with more traditional approaches. Johannes Gehrke, a managing director at Microsoft Research who also heads up machine learning efforts for Microsoft Teams, says that his work opens up many interesting applications, such as doing so-called “multidimensional queries” in main-memory data warehouses. Harvard’s Idreos also expects the project to spur further work on how to maintain the good performance of such systems when new data and new kinds of queries arrive. Bao is short for “bandit optimizer,” a play on words related to the so-called “multi-armed bandit” analogy where a gambler tries to maximize their winnings at multiple slot machines that have different rates of return. The multi-armed bandit problem is commonly found in any situation that has tradeoffs between exploring multiple different options, versus exploiting a single option — from risk optimization to A/B testing. “Query optimizers have been around for years, but they often make mistakes, and usually they don’t learn from them,” says Kraska. “That’s where we feel that our system can make key breakthroughs, as it can quickly learn for the given data and workload what query plans to use and which ones to avoid.” Kraska says that in contrast to other learning-based approaches to query optimization, Bao learns much faster and can outperform open-source and commercial optimizers with as little as one hour of training time.In the future, his team aims to integrate Bao into cloud systems to improve resource utilization in environments where disk, RAM, and CPU time are scarce resources. “Our hope is that a system like this will enable much faster query times, and that people will be able to answer questions they hadn’t been able to answer before,” says Kraska. A related paper about Tsunami was co-written by Kraska, PhD students Jialin Ding and Vikram Nathan, and MIT Professor Mohammad Alizadeh. A paper about Bao was co-written by Kraska, Marcus, PhD students Parimarjan Negi and Hongzi Mao, visiting scientist Nesime Tatbul, and Alizadeh. The work was done as part of the Data System and AI Lab (DSAIL@CSAIL), which is sponsored by Intel, Google, Microsoft, and the U.S. National Science Foundation.  One of the biggest challenges in computing is handling a staggering onslaught of information while still being able to efficiently store and process it. https://news.mit.edu/2020/masks-mandates-impact-deaths-0805 Analysis shows requiring masks for public-facing U.S. business employees on April 1 would have saved tens of thousands of lives. Wed, 05 Aug 2020 00:00:00 -0400 https://news.mit.edu/2020/masks-mandates-impact-deaths-0805 Peter Dizikes | MIT News Office The research described in this article has been published as a working paper but has not yet been peer-reviewed by experts in the field. Masks reduce the spread of Covid-19. But just how much of an effect do they have? A study co-authored by an MIT professor finds that if the U.S. had introduced a uniform national mask mandate for employees of public-facing businesses on April 1, the number of deaths in the U.S. would likely have been 40 percent lower on June 1. “It is a very effective policy that includes relatively little economic disruption,” says Victor Chernozhukov, a professor in the Department of Economics and the Statistics and Data Science Center at MIT, and one of the authors of a paper detailing the study’s results. “We found it produced a considerable reduction in fatalities.” Among other findings about the ongoing pandemic, drawing on the timing of state policy announcements, medical data, and Google mobility data, the study also shows that in the same timeframe, total Covid-19 cases in the U.S. would have likely been 80 percent higher without the stay-at-home orders implemented by the vast majority of states. Additionally, the researchers evaluated how much the reduction in people’s movement — such as commuting and shopping trips — has followed specific state policies, and how much has stemmed from personal decisions to stay home more often. Their conclusion is that each factor accounts for about half of the decline in physical movement during the pandemic. The paper, “Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S.,” has been posted on the MedRxiv preprint server and as part of the Covid Economics paper series by the Center for Economic Policy Research in London. The authors are Chernozhukov; Hiroyuki Kasahara, a professor at the Vancouver School of Economics at the University of British Columbia; and Paul Schrimpf, an associate professor at the Vancouver School of Economics at the University of British Columbia. State variation creates room for study To conduct the study, the economists took advantage of the fact that many U.S. states have implemented mask mandates at different times this year. By examining the before-and-after trajectories of cases and deaths, the study was able to identify the impact of the mask mandates. To be sure, states also differ from each other in numerous ways that may influence the spread of Covid-19, including demographic factors such as the age and health of state residents; population density; additional state-level policies curbing the spread of Covid-19; and self-directed changes in population movement, in response to the pandemic. The study also accounted for the fact that Covid-19 testing increased during this time. “The results hold up,” Chernozhukov says. “Controlling for behavior, information variables, confounding factors — the mask mandates are critical to the decline in deaths. No matter how we look at the data, that result is there.” Specifically, after accounting for those circumstances, the researchers estimated that mask mandates would have produced a 40 percent reduction in deaths, nationally. That finding had a 90 percent confidence interval, which describes the likely range of estimated outcomes. That means mandated mask-wearing would have reduced U.S. fatalities by anywhere from 17,000 to 55,000 from April 1 through June 1. The 80 percent reduction in cases the researchers attributed to stay-at-home orders also had a 90 percent confidence interval, implying that those policies reduced the overall number of cases by anywhere from 500,000 to 3.4 million between April 1 and June 1. Accounting for movement In assessing the relationship between public policy and the course of the Covid-19 pandemic, the researchers used Google Mobility Reports data to assess a related question: To what extent does people’s behavior respond to state policy mandates, or to what extent does it stem from “private” or self-directed decisions, based on other information about the public-health situation? The Google data included mobility measures showing the prevalence of visits to public transit, grocery stores, other retail establishments, and workplaces. Ultimately — again based on the timing of changes in mobility patterns in relation to state-level stay-at-home directives — the researchers concluded that adherence to government mandates accounts for about half of the observed reductions in travel. “We compute that the policies played an important role, but also that the private responses of people played an equally important role,” Chernozhukov says. “It’s a combination of the two.” The researchers note that they could not measure the effects of all important policy decisions on the reduction of Covid-19 transmission. Consider the school closures that occurred almost nationwide in mid-March: Because the timing of that policy change was so similar across the country, it is very difficult to estimate its effects. If some states had left their schools open longer, it would be easier to quantify what difference the closures made. “We couldn’t reliably answer that question with our data because the school closures happened almost in one week, with very little variation,” Chernozhukov observes. However, given that many states have continued changing their policies after June 1, with significant variation in state-level mask policies and economic reopening plans, the scholars say they are continuing to study the subject, and plan to release more findings about it in the near future. “We are continuing to analyze these issues, and we hope to produce another paper that focuses on the effects of mask mandates during the reopening phase,” Chernozhukov says. A study co-authored by an MIT professor finds that if the U.S. had introduced a uniform national mask mandate for employees of public-facing businesses on April 1, the number of deaths in the U.S. would likely have been 40 percent lower on June 1. Image: stock photo https://news.mit.edu/2020/machine-learning-health-care-system-understands-when-to-step-in-0731 Machine learning system from MIT CSAIL can look at chest X-rays to diagnose pneumonia — and also knows when to defer to a radiologist. Fri, 31 Jul 2020 14:15:01 -0400 https://news.mit.edu/2020/machine-learning-health-care-system-understands-when-to-step-in-0731 Adam Conner-Simons | MIT CSAIL In recent years, entire industries have popped up that rely on the delicate interplay between human workers and automated software. Companies like Facebook work to keep hateful and violent content off their platforms using a combination of automated filtering and human moderators. In the medical field, researchers at MIT and elsewhere have used machine learning to help radiologists better detect different forms of cancer.  What can be tricky about these hybrid approaches is understanding when to rely on the expertise of people versus programs. This isn’t always merely a question of who does a task “better;” indeed, if a person has limited bandwidth, the system may have to be trained to minimize how often it asks for help. To tackle this complex issue, researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have developed a machine learning system that can either make a prediction about a task, or defer the decision to an expert. Most importantly, it can adapt when and how often it defers to its human collaborator, based on factors such as its teammate’s availability and level of experience. The team trained the system on multiple tasks, including looking at chest X-rays to diagnose specific conditions such as atelectasis (lung collapse) and cardiomegaly (an enlarged heart). In the case of cardiomegaly, they found that their human-AI hybrid model performed 8 percent better than either could on their own (based on AU-ROC scores).   “In medical environments where doctors don’t have many extra cycles, it’s not the best use of their time to have them look at every single data point from a given patient’s file,” says PhD student Hussein Mozannar, lead author with David Sontag, the Von Helmholtz Associate Professor of Medical Engineering in the Department of Electrical Engineering and Computer Science, of a new paper about the system that was recently presented at the International Conference of Machine Learning. “In that sort of scenario, it’s important for the system to be especially sensitive to their time and only ask for their help when absolutely necessary.” The system has two parts: a “classifier” that can predict a certain subset of tasks, and a “rejector” that decides whether a given task should be handled by either its own classifier or the human expert. Through experiments on tasks in medical diagnosis and text/image classification, the team showed that their approach not only achieves better accuracy than baselines, but does so with a lower computational cost and with far fewer training data samples. “Our algorithms allow you to optimize for whatever choice you want, whether that’s the specific prediction accuracy or the cost of the expert’s time and effort,” says Sontag, who is also a member of MIT’s Institute for Medical Engineering and Science. “Moreover, by interpreting the learned rejector, the system provides insights into how experts make decisions, and in which settings AI may be more appropriate, or vice-versa.” The system’s particular ability to help detect offensive text and images could also have interesting implications for content moderation. Mozanner suggests that it could be used at companies like Facebook in conjunction with a team of human moderators. (He is hopeful that such systems could minimize the amount of hateful or traumatic posts that human moderators have to review every day.) Sontag clarified that the team has not yet tested the system with human experts, but instead developed a series of “synthetic experts” so that they could tweak parameters such as experience and availability. In order to work with a new expert it’s never seen before, the system would need some minimal onboarding to get trained on the person’s particular strengths and weaknesses. In future work, the team plans to test their approach with real human experts, such as radiologists for X-ray diagnosis. They will also explore how to develop systems that can learn from biased expert data, as well as systems that can work with — and defer to — several experts at once. For example, Sontag imagines a hospital scenario where the system could collaborate with different radiologists who are more experienced with different patient populations. “There are many obstacles that understandably prohibit full automation in clinical settings, including issues of trust and accountability,” says Sontag. “We hope that our method will inspire machine learning practitioners to get more creative in integrating real-time human expertise into their algorithms.”  Mozanner is affiliated with both CSAIL and the MIT Institute for Data, Systems and Society (IDSS). The team’s work was supported, in part, by the National Science Foundation. The system either queries the expert to diagnose the patient based on their X-ray and medical records, or looks at the X-ray to make the diagnosis itself. Image courtesy of MIT CSAIL. https://news.mit.edu/2020/tenured-mit-engineers-0724 Eight faculty members have been granted tenure in five departments across the School of Engineering. Fri, 24 Jul 2020 15:55:01 -0400 https://news.mit.edu/2020/tenured-mit-engineers-0724 School of Engineering The School of Engineering has announced that MIT has granted tenure to eight members of its faculty in the departments of Civil and Environmental Engineering, Chemical Engineering, Electrical Engineering and Computer Science, Mechanical Engineering, and Nuclear Science and Engineering. “This year’s newly tenured faculty in the School of Engineering are truly inspiring,” says Anantha P. Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Their dedication to research and teaching drives novel solutions urgently needed to advance their fields.” This year’s newly tenured associate professors are: Lydia Bourouiba, in the Department of Civil and Environmental Engineering, the Department of Mechanical Engineering, and the Institute for Medical Engineering and Science, focuses her expertise as a physical applied mathematician on problems at the interface of fluid dynamics and infectious disease transmission. Her work leverages advanced fluid dynamic experiments at various scales, algorithms, and mathematical modeling to understand the physical mechanisms shaping disease transmission dynamics, epidemics, and pandemics in human, animal, and plant populations. Motivated by problems in these application domains, her work elucidates fundamental multiscale dynamics of fluid fragmentation, mixing, and transport processes where interfacial, multi-phase, biological, and complex fluids and flows are determining pathogen dispersal and persistence in a range of environments. Fikile Brushett, the Cecil and Ida Green Career Development Professor in the Department of Chemical Engineering, focuses his research on advancing the science and engineering of electrochemical technologies for a sustainable energy economy. He is especially fascinated by the fundamental processes that define the performance, cost, and lifetime of present-day and future electrochemical systems. Thomas Heldt, in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, focuses his research on signal processing, mathematical modeling, and model identification to understand the physiology of the injured brain and to support real-time clinical decision-making, monitoring of disease progression, and titration of therapy. His research is conducted in close collaboration with clinicians from Boston-area hospitals — particularly in emergency, neonatal and neurocritical care — where his team is integrally involved in designing and deploying high-fidelity data-acquisition systems and in collecting clinical data.  Asegun Henry is the Robert N. Noyce Career Development Professor in the Department of Mechanical Engineering. His primary research is in heat transfer, with an emphasis on understanding the science of energy transport, storage and conversion at the atomic level, along with the development of new industrial-scale energy technologies to mitigate climate change. He has made significant advances and contributions to several fields within energy and heat transfer, namely: solar fuels and thermochemistry, phonon transport in disordered materials, phonon transport at interfaces, and he has developed the highest-temperature pump on record, which used an all-ceramic mechanical pump to pump liquid metal above 1,400 degrees Celsius. William Oliver, in the Department of Electrical Engineering and Computer Science, works with the Quantum Information and Integrated Nanosystems Group at Lincoln Laboratory and the Engineering Quantum Systems Group at MIT, where he provides programmatic and technical leadership for programs related to the development of quantum and classical high-performance computing technologies for quantum information science applications. His interests include the materials growth, fabrication, design, and control of superconducting quantum processors, as well as the development of cryogenic packaging and control electronics involving cryogenic CMOS and single-flux quantum digital logic. He is director of the Center for Quantum Engineering and associate director of the Research Laboratory of Electronics. Michael Short, the Class of 1942 Career Development Professor in the Department of Nuclear Science and Engineering, develops new materials and measurement methods to usher in the next generation of safe and scalable nuclear power. He is currently focused on choosing and proving structural materials for fusion reactors, creating tools to measure tiny amounts of radiation damage for nuclear non-proliferation, and stopping corrosion and fouling in the most extreme energy production environments. Vivienne Sze, in the Department of Electrical Engineering and Computer Science, focuses her research on designing and implementing computing systems that enable energy-efficient machine learning, computer vision, and video compression for a wide range of applications, including autonomous navigation, digital health, and the internet of things. In particular, she is interested in the joint design of algorithms, architectures, circuits, and systems to enable optimal tradeoffs between energy consumption, speed, and quality of results.  Caroline Uhler, in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, focuses her research at the intersection of machine learning, statistics, and genomics. In particular, she is interested in obtaining a better understanding of genome regulation by developing machine learning methods that can integrate different data modalities, including interventional data, and bridge the gap from predictive to causal modeling. Top row, left to right: Lydia Bourouiba, Thomas Heldt, Fikile Brushett, and Asegun Henry. Bottom row, left to right: William Oliver, Michael Short, Vivienne Sze, and Caroline Uhler. Photos: Lillie Paquette/School of Engineering https://news.mit.edu/2020/faculty-receive-funding-develop-novel-ai-techniques-combat-covid-19-0717 C3.ai Digital Transformation Institute awards $5.4 million to top researchers to steer how society responds to the pandemic. Fri, 17 Jul 2020 15:30:01 -0400 https://news.mit.edu/2020/faculty-receive-funding-develop-novel-ai-techniques-combat-covid-19-0717 School of Engineering | MIT Schwarzman College of Computing Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread. Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy. The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing. “We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics,” says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.” Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays. Bertsimas’s project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglar’s research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahleh’s research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes. “The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapid start and significant progress of the C3.ai Digital Transformation Institute,” says Thomas Siebel, chair and CEO of C3.ai. “It is a privilege to be working with such an accomplished team.” The following projects are the MIT recipients of the inaugural C3.ai DTI Awards:  “Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions” — Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science “Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data” — Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University) “Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic” — Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics “Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak” — Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering “Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections” — David Gifford, professor of biological engineering and of electrical engineering and computer science “Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19” — Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago) “Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic” — Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy. https://news.mit.edu/2020/learners-today-leaders-tomorrow-0713 MITx MicroMasters Program credential holders leverage MIT-caliber education to move their industries to the cutting edge. Mon, 13 Jul 2020 16:00:01 -0400 https://news.mit.edu/2020/learners-today-leaders-tomorrow-0713 MIT Open Learning On June 18, 609 learners celebrated the completion of MITx MicroMasters programs in Data, Economics, and Development Policy (DEDP), Principles of Manufacturing, and Statistics and Data Science in an online event hosted by MIT Open Learning. With Vice President for Open Learning Professor Sanjay Sarma presiding, the celebration emphasized the credential holders’ tenacity and potential to transform their industries and communities. This is the first time cohorts from these three programs have been recognized through a completion ceremony, bringing together learners from 82 countries who earned their credentials between 2018 and 2020. Housed at the Office of Open Learning, the MicroMasters programs are created in conjunction with departments, labs, and centers all over MIT: DEDP is offered jointly through the Department of Economics and the Abdul Latif Jameel Poverty Action Lab (J-PAL), Principles of Manufacturing through the Department of Mechanical Engineering, and Statistics and Data Science through the Institute for Data, Systems, and Society (IDSS). “Learning online requires a lot of self-discipline and perseverance,” notes Esther Duflo, the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics. “You have really achieved something very difficult and quite remarkable, that shows both your talent and your commitment.” Opening doors, broadening perspectives The ceremony, like the certificate itself, means something different to each recipient. For some, it’s a private achievement, signaling a mastery of a subject that holds deep personal significance. For others, it’s a momentous step in reaching career goals. Still others view the MicroMasters program as a route toward an education they couldn’t otherwise achieve: 70 percent of DEDP learners, for example, come from middle- to low-income countries. The MicroMasters learners honored at this year’s ceremony represent a wide variety of professionals at all stages of their careers. David Bruce, a liver transplant surgeon at the Ochsner Clinic in New Orleans, Louisiana, enrolled in the Statistics and Data Science program out of interest in the subject, and is now applying his new knowledge in transplant informatics. Fabio Castro, a learner in Brazil who completed the same program, was inspired to apply for MIT’s PhD program in civil engineering, and he will matriculate in the fall.  Some of the ceremony’s honorees were already avid online learners before beginning their MicroMasters journey. Linxi Wang, a DEDP learner currently based in the United States, described how valuable it is to be connected to a global network of like-minded professionals: “One thing I absolutely love and couldn’t find anywhere else is the friendly community we’ve built.” Badri Ratnam, who received the Principles of Manufacturing credential this year, had earned upwards of 25 MITx certificates in several subjects, and was thrilled to discover that he could use his learning to advance his engineering career. “When I saw that MITx was offering a mechanical engineering-related MicroMasters, I jumped at the opportunity,” he says. “The value of this program to me is that it helped me understand the challenges in bringing a prototype product to the world — I’m engaged in one such project at work as we speak.” Now having completed his credential, Ratnam hopes to continue his studies, perhaps earning dual degrees in manufacturing and supply chain management. Many credential holders have been able to apply their knowledge to pressing global issues. Australia-based learner David Fong described how the DEDP program helped inform his work in child development and health-care screening with the nonprofit Spur Afrika in Nairobi, Kenya. Eva Flonner, a learner in Austria, could not have found a more urgent use for her new skills in Statistics and Data Science: “The MIT MicroMasters program really changed my career, since I’m now responsible for data science tasks linked to the corona crisis,” she says. A new way forward for global education In addition celebrating the personal achievements of individual credential holders, the ceremony is testament to the possibilities offered by a new way forward in education. MIT launched the MicroMasters program in 2016, the first of its kind in the world. It began as a means of disrupting the traditional admissions process for the master’s degree program in supply chain management: anyone who earned the online credential, requiring the successful completion of a curated suite of MITx courses, would be eligible to finish their degree on campus, without needing to meet any other admissions criteria.  The microcredential model has been replicated at dozens of global universities in recent years, both as an alternate graduate admissions route and as a means of awarding respected professional credentials for high-demand fields. In his remarks, Professor Devavrat Shah, director of MIT’s Statistics and Data Science Center, commented on how much the field of higher education can learn from implementing microcredential programs: “[MicroMasters learners] have become model students for us as we, universities across the globe, grapple with Covid-19 and think about how we deal with blended and online education,” he said. The MIT MicroMasters program continues to grow, with credentials awarded to 2,852 individual learners in four different disciplines, and with pathways to graduate degrees at collaborating universities around the world. A fifth program, the new MicroMasters in Finance from MIT Sloan School of Management, will begin running courses in September.  David Hardt, professor of mechanical engineering, echoed the sentiments of all the MicroMasters ceremony speakers, expressing a wish that each credential holder will use their new skills and knowledge to become a leader in their field, “someone people will look to” for knowledge and guidance. Remarking on how closely the MicroMasters mirrors the rigor of a residential MIT graduate program, Hardt said, “You’ve done a marvelous thing.”  Says Professor Krishna Rajagopal, dean for digital learning, “This celebration served as an affirmation that amidst so much uncertainty, people can still accomplish great things. I have no doubt that MicroMasters learners will help lead us through these challenging times and into a brighter future.” During the MicroMasters completion celebration, credential holder David Fong describes how his work with the nonprofit Spur Afrika was informed by his coursework in Data, Economics, and Development Policy. Photo courtesy of the MicroMasters completion ceremony. https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 A global team of researchers searches for insights during a weeklong virtual “datathon.” Wed, 01 Jul 2020 11:20:01 -0400 https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 Kim Martineau | MIT Quest for Intelligence Uncertainty about the course of the Covid-19 pandemic continues, with more than 2,500,000 known cases and 126,000 deaths in the United States alone. How to contain the virus, limit its damage, and address the deep-rooted health and racial inequalities it has exposed are now urgent topics for policymakers. Earlier this spring, 300 data scientists and health care professionals from around the world joined the MIT Covid-19 Datathon to see what insights they might uncover. “It felt important to be a part of,” says Ashley O’Donoghue, an economist at the Center for Healthcare Delivery Science at Beth Israel Deaconess Medical Center. “We thought we could produce something that might make a difference.” Participants were free to explore five tracks: the epidemiology of Covid-19, its policy impacts, its disparate health outcomes, the pandemic response in New York City, and the wave of misinformation Covid-19 has spawned. After splitting into teams, participants were set loose on 20 datasets, ranging from county-level Covid-19 cases compiled by The New York Times to a firehose of pandemic-related posts released by Twitter.  The participants, and the dozens of mentors who guided them, hailed from 44 countries and every continent except for Antarctica. To encourage the sharing of ideas and validation of results, the event organizers — MIT Critical Data, MIT Hacking Medicine, and the Martin Trust Center for MIT Entrepreneurship — required that all code be made available. In the end, 47 teams presented final projects, and 10 were singled out for recognition by a panel of judges. Several teams are now writing up their results for peer-reviewed publication, and at least one team has posted a paper. “It’s really hard to find research collaborators, especially during a crisis,” says Marie-Laure Charpignon, a PhD student with MIT’s Institute for Data, Systems, and Society, who co-organized the event. “We’re hoping that the teams and mentors that found each other will continue to explore these questions.” In a pre-print on medRxiv, O’Donoghue and her teammates identify the businesses most at risk for seeding new Covid-19 infections in New York, California, and New England. Analyzing location data from SafeGraph, a company that tracks commercial foot traffic, the team built a transmission-risk index for businesses that in the first five months of this year drew the most customers, for longer periods of time, and in more crowded conditions, due to their modest size.  Comparing this risk index to new weekly infections, the team classified 16.3 percent of countywide businesses as “superspreaders,” most of which were restaurants and hotels. A 1 percent increase in the density of super-spreader businesses, they found, was linked to a 5 percent jump in Covid-19 cases. The team is now extending its analysis to all 50 states, drilling down to ZIP code-level data, and building a decision-support tool to help several hospitals in their sample monitor risk as communities reopen. The tool will also let policymakers evaluate a wide range of statewide reopening policies. “If we see a second wave of infections, we can determine which policies actually worked,” says O’Donoghue. The datathon model for collaborative research is the brainchild of Leo Anthony Celi, a researcher at MIT and staff physician at Beth Israel Deaconess Medical Center. The events are usually coffee-fueled weekend affairs. But this one took place over a work week, and amid a global lockdown, with teammates having to meet and collaborate over Slack and Zoom. With no coffee breaks or meals, they had fewer chances to network, says Celi. But the virtual setting allowed more people to join, especially mentors, who could participate without taking time off to travel. It also may have made teams more efficient, he says.  After analyzing communication logs from the event, he and his colleagues found evidence that the most-successful teams lacked a clear leader. Everyone seemed to chip in. “In face-to-face events, leaders and followers emerge as they project their expertise and personalities,” he says. “But on Slack, we saw less hierarchy. The most successful teams showed high levels of enthusiasm and conversational turn-taking.” Another advantage of the virtual setting is that teams straddling several time zones could work, literally, around the clock. “You could post a message on Slack and someone would see it an hour or two later,” says Jane E. Valentine, a biomedical engineer at the Johns Hopkins University Applied Physics Laboratory. “There was a constant sense of engagement. I might be sleeping and doing nothing, but the wheels were still turning.” Valentine collaborated with a doctor and three data scientists in Europe, the United States, and Canada to analyze anonymized medical data from 4,000 Covid-19 patients to build predictive models for how long a new patient might need to be hospitalized, and their likelihood of dying. “It’s really useful for a clinician to know if a patient is likely to stabilize or go downhill,” she says. “You may want to monitor or treat them more aggressively.” Hospital administrators can also decide whether to open up additional wards, she adds. Among their findings, the team found that a fever and shortness of breath were top symptoms for predicting both a long hospital stay and a high risk of death for patients, and that general respiratory symptoms were also a strong predictor of death. Valentine cautions that the results are preliminary, and based on incomplete data that the team is currently working to fill.  One of the pandemic’s cruel realities is that it has hit the poorest and most vulnerable people in society hardest. Datathon participants also examined Covid-19’s social impact, from analyzing the impact of releasing prisoners to devising tools for people to verify the flood of claims about the disease now circulating online.  Amber Nigam, a data scientist based in New Delhi, India, has watched conspiracy theories spread and multiply on social media as contagiously as Covid-19 itself. “There’s a lot of anxiety,” he says. “Even my parents have shown me news on WhatsApp and asked if it was true.”  As the head of AI for PeopleStrong, a predictive sales startup in San Francisco, California, Nigam is comfortable with natural language processing tools and interested in their potential for fighting fake news. During the datathon, he and his team crawled the web for conspiracy theories circulating in the United States, China, and India, among other countries, and used the data to build an automated fact-checker. If the tool finds the claim to be untrue, it sends the reader to the news source where the claim was first debunked.  “A lot of people in rural settings don’t have access to accurate sources of information,” he says. “It’s super critical for people to have the right facts at their disposal.” Another team looked at Covid-19’s disparate impact on people of color. Lauren Chambers, a technology fellow at the Massachusetts American Civil Liberties Union (ACLU), suggested the project and mentored the team that took it on. State by state, the team found disproportionate death rates among Black and Hispanic people, who are more likely to work “essential” service-industry jobs where they face greater exposure to people infected with the disease. The gap was greatest in South Carolina, where Black individuals account for about half of Covid-19 deaths, but only a third of residents. The team noted that the picture nationally is probably worse, given that 10 states still do not collect race-specific data.  The team also found that poverty and lack of health care access were linked to higher death rates among Black communities, and language barriers were linked to higher death rates among Hispanic individuals. Their findings suggest that economic interventions for Black Americans, and hiring more hospital translators for Hispanic Americans, might be effective policies to reduce inequities in health outcomes. The ACLU can’t afford to hire an army of data scientists to investigate every civil-rights violation the pandemic has brought to light, says Chambers. But collaborative events like this one give community advocates a chance to explore urgent questions they wouldn’t otherwise be able to, she says, and data scientists get to hear new perspectives, too. “There’s a dangerous tendency among data scientists to think that numbers are the beginning and end of any good analysis,” she says. “But data are subjective, and there’s all kinds of other expertise that communities hold.” The event was sponsored by Beth Israel Deaconess Medical Center Innovation Group, Google Cloud, Massachusetts ACLU, and the National Science Foundation’s West Big Data Innovation Hub. A virtual “datathon” organized by MIT to bring fresh insights to the Covid-19 pandemic drew 300 participants and 44 mentors from around the world. Here, mentors who volunteered to judge the final projects meet on Zoom to select the top 10 projects. Image: Leo Anthony Celi https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 A global team of researchers searches for insights during a weeklong virtual “datathon.” Wed, 01 Jul 2020 11:20:01 -0400 https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 Kim Martineau | MIT Quest for Intelligence Uncertainty about the course of the Covid-19 pandemic continues, with more than 2,500,000 known cases and 126,000 deaths in the United States alone. How to contain the virus, limit its damage, and address the deep-rooted health and racial inequalities it has exposed are now urgent topics for policymakers. Earlier this spring, 300 data scientists and health care professionals from around the world joined the MIT Covid-19 Datathon to see what insights they might uncover. “It felt important to be a part of,” says Ashley O’Donoghue, an economist at the Center for Healthcare Delivery Science at Beth Israel Deaconess Medical Center. “We thought we could produce something that might make a difference.” Participants were free to explore five tracks: the epidemiology of Covid-19, its policy impacts, its disparate health outcomes, the pandemic response in New York City, and the wave of misinformation Covid-19 has spawned. After splitting into teams, participants were set loose on 20 datasets, ranging from county-level Covid-19 cases compiled by The New York Times to a firehose of pandemic-related posts released by Twitter.  The participants, and the dozens of mentors who guided them, hailed from 44 countries and every continent except for Antarctica. To encourage the sharing of ideas and validation of results, the event organizers — MIT Critical Data, MIT Hacking Medicine, and the Martin Trust Center for MIT Entrepreneurship — required that all code be made available. In the end, 47 teams presented final projects, and 10 were singled out for recognition by a panel of judges. Several teams are now writing up their results for peer-reviewed publication, and at least one team has posted a paper. “It’s really hard to find research collaborators, especially during a crisis,” says Marie-Laure Charpignon, a PhD student with MIT’s Institute for Data, Systems, and Society, who co-organized the event. “We’re hoping that the teams and mentors that found each other will continue to explore these questions.” In a pre-print on medRxiv, O’Donoghue and her teammates identify the businesses most at risk for seeding new Covid-19 infections in New York, California, and New England. Analyzing location data from SafeGraph, a company that tracks commercial foot traffic, the team built a transmission-risk index for businesses that in the first five months of this year drew the most customers, for longer periods of time, and in more crowded conditions, due to their modest size.  Comparing this risk index to new weekly infections, the team classified 16.3 percent of countywide businesses as “superspreaders,” most of which were restaurants and hotels. A 1 percent increase in the density of super-spreader businesses, they found, was linked to a 5 percent jump in Covid-19 cases. The team is now extending its analysis to all 50 states, drilling down to ZIP code-level data, and building a decision-support tool to help several hospitals in their sample monitor risk as communities reopen. The tool will also let policymakers evaluate a wide range of statewide reopening policies. “If we see a second wave of infections, we can determine which policies actually worked,” says O’Donoghue. The datathon model for collaborative research is the brainchild of Leo Anthony Celi, a researcher at MIT and staff physician at Beth Israel Deaconess Medical Center. The events are usually coffee-fueled weekend affairs. But this one took place over a work week, and amid a global lockdown, with teammates having to meet and collaborate over Slack and Zoom. With no coffee breaks or meals, they had fewer chances to network, says Celi. But the virtual setting allowed more people to join, especially mentors, who could participate without taking time off to travel. It also may have made teams more efficient, he says.  After analyzing communication logs from the event, he and his colleagues found evidence that the most-successful teams lacked a clear leader. Everyone seemed to chip in. “In face-to-face events, leaders and followers emerge as they project their expertise and personalities,” he says. “But on Slack, we saw less hierarchy. The most successful teams showed high levels of enthusiasm and conversational turn-taking.” Another advantage of the virtual setting is that teams straddling several time zones could work, literally, around the clock. “You could post a message on Slack and someone would see it an hour or two later,” says Jane E. Valentine, a biomedical engineer at the Johns Hopkins University Applied Physics Laboratory. “There was a constant sense of engagement. I might be sleeping and doing nothing, but the wheels were still turning.” Valentine collaborated with a doctor and three data scientists in Europe, the United States, and Canada to analyze anonymized medical data from 4,000 Covid-19 patients to build predictive models for how long a new patient might need to be hospitalized, and their likelihood of dying. “It’s really useful for a clinician to know if a patient is likely to stabilize or go downhill,” she says. “You may want to monitor or treat them more aggressively.” Hospital administrators can also decide whether to open up additional wards, she adds. Among their findings, the team found that a fever and shortness of breath were top symptoms for predicting both a long hospital stay and a high risk of death for patients, and that general respiratory symptoms were also a strong predictor of death. Valentine cautions that the results are preliminary, and based on incomplete data that the team is currently working to fill.  One of the pandemic’s cruel realities is that it has hit the poorest and most vulnerable people in society hardest. Datathon participants also examined Covid-19’s social impact, from analyzing the impact of releasing prisoners to devising tools for people to verify the flood of claims about the disease now circulating online.  Amber Nigam, a data scientist based in New Delhi, India, has watched conspiracy theories spread and multiply on social media as contagiously as Covid-19 itself. “There’s a lot of anxiety,” he says. “Even my parents have shown me news on WhatsApp and asked if it was true.”  As the head of AI for PeopleStrong, a predictive sales startup in San Francisco, California, Nigam is comfortable with natural language processing tools and interested in their potential for fighting fake news. During the datathon, he and his team crawled the web for conspiracy theories circulating in the United States, China, and India, among other countries, and used the data to build an automated fact-checker. If the tool finds the claim to be untrue, it sends the reader to the news source where the claim was first debunked.  “A lot of people in rural settings don’t have access to accurate sources of information,” he says. “It’s super critical for people to have the right facts at their disposal.” Another team looked at Covid-19’s disparate impact on people of color. Lauren Chambers, a technology fellow at the Massachusetts American Civil Liberties Union (ACLU), suggested the project and mentored the team that took it on. State by state, the team found disproportionate death rates among Black and Hispanic people, who are more likely to work “essential” service-industry jobs where they face greater exposure to people infected with the disease. The gap was greatest in South Carolina, where Black individuals account for about half of Covid-19 deaths, but only a third of residents. The team noted that the picture nationally is probably worse, given that 10 states still do not collect race-specific data.  The team also found that poverty and lack of health care access were linked to higher death rates among Black communities, and language barriers were linked to higher death rates among Hispanic individuals. Their findings suggest that economic interventions for Black Americans, and hiring more hospital translators for Hispanic Americans, might be effective policies to reduce inequities in health outcomes. The ACLU can’t afford to hire an army of data scientists to investigate every civil-rights violation the pandemic has brought to light, says Chambers. But collaborative events like this one give community advocates a chance to explore urgent questions they wouldn’t otherwise be able to, she says, and data scientists get to hear new perspectives, too. “There’s a dangerous tendency among data scientists to think that numbers are the beginning and end of any good analysis,” she says. “But data are subjective, and there’s all kinds of other expertise that communities hold.” The event was sponsored by Beth Israel Deaconess Medical Center Innovation Group, Google Cloud, Massachusetts ACLU, and the National Science Foundation’s West Big Data Innovation Hub. A virtual “datathon” organized by MIT to bring fresh insights to the Covid-19 pandemic drew 300 participants and 44 mentors from around the world. Here, mentors who volunteered to judge the final projects meet on Zoom to select the top 10 projects. Image: Leo Anthony Celi https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 A global team of researchers searches for insights during a weeklong virtual “datathon.” Wed, 01 Jul 2020 11:20:01 -0400 https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 Kim Martineau | MIT Quest for Intelligence Uncertainty about the course of the Covid-19 pandemic continues, with more than 2,500,000 known cases and 126,000 deaths in the United States alone. How to contain the virus, limit its damage, and address the deep-rooted health and racial inequalities it has exposed are now urgent topics for policymakers. Earlier this spring, 300 data scientists and health care professionals from around the world joined the MIT Covid-19 Datathon to see what insights they might uncover. “It felt important to be a part of,” says Ashley O’Donoghue, an economist at the Center for Healthcare Delivery Science at Beth Israel Deaconess Medical Center. “We thought we could produce something that might make a difference.” Participants were free to explore five tracks: the epidemiology of Covid-19, its policy impacts, its disparate health outcomes, the pandemic response in New York City, and the wave of misinformation Covid-19 has spawned. After splitting into teams, participants were set loose on 20 datasets, ranging from county-level Covid-19 cases compiled by The New York Times to a firehose of pandemic-related posts released by Twitter.  The participants, and the dozens of mentors who guided them, hailed from 44 countries and every continent except for Antarctica. To encourage the sharing of ideas and validation of results, the event organizers — MIT Critical Data, MIT Hacking Medicine, and the Martin Trust Center for MIT Entrepreneurship — required that all code be made available. In the end, 47 teams presented final projects, and 10 were singled out for recognition by a panel of judges. Several teams are now writing up their results for peer-reviewed publication, and at least one team has posted a paper. “It’s really hard to find research collaborators, especially during a crisis,” says Marie-Laure Charpignon, a PhD student with MIT’s Institute for Data, Systems, and Society, who co-organized the event. “We’re hoping that the teams and mentors that found each other will continue to explore these questions.” In a pre-print on medRxiv, O’Donoghue and her teammates identify the businesses most at risk for seeding new Covid-19 infections in New York, California, and New England. Analyzing location data from SafeGraph, a company that tracks commercial foot traffic, the team built a transmission-risk index for businesses that in the first five months of this year drew the most customers, for longer periods of time, and in more crowded conditions, due to their modest size.  Comparing this risk index to new weekly infections, the team classified 16.3 percent of countywide businesses as “superspreaders,” most of which were restaurants and hotels. A 1 percent increase in the density of super-spreader businesses, they found, was linked to a 5 percent jump in Covid-19 cases. The team is now extending its analysis to all 50 states, drilling down to ZIP code-level data, and building a decision-support tool to help several hospitals in their sample monitor risk as communities reopen. The tool will also let policymakers evaluate a wide range of statewide reopening policies. “If we see a second wave of infections, we can determine which policies actually worked,” says O’Donoghue. The datathon model for collaborative research is the brainchild of Leo Anthony Celi, a researcher at MIT and staff physician at Beth Israel Deaconess Medical Center. The events are usually coffee-fueled weekend affairs. But this one took place over a work week, and amid a global lockdown, with teammates having to meet and collaborate over Slack and Zoom. With no coffee breaks or meals, they had fewer chances to network, says Celi. But the virtual setting allowed more people to join, especially mentors, who could participate without taking time off to travel. It also may have made teams more efficient, he says.  After analyzing communication logs from the event, he and his colleagues found evidence that the most-successful teams lacked a clear leader. Everyone seemed to chip in. “In face-to-face events, leaders and followers emerge as they project their expertise and personalities,” he says. “But on Slack, we saw less hierarchy. The most successful teams showed high levels of enthusiasm and conversational turn-taking.” Another advantage of the virtual setting is that teams straddling several time zones could work, literally, around the clock. “You could post a message on Slack and someone would see it an hour or two later,” says Jane E. Valentine, a biomedical engineer at the Johns Hopkins University Applied Physics Laboratory. “There was a constant sense of engagement. I might be sleeping and doing nothing, but the wheels were still turning.” Valentine collaborated with a doctor and three data scientists in Europe, the United States, and Canada to analyze anonymized medical data from 4,000 Covid-19 patients to build predictive models for how long a new patient might need to be hospitalized, and their likelihood of dying. “It’s really useful for a clinician to know if a patient is likely to stabilize or go downhill,” she says. “You may want to monitor or treat them more aggressively.” Hospital administrators can also decide whether to open up additional wards, she adds. Among their findings, the team found that a fever and shortness of breath were top symptoms for predicting both a long hospital stay and a high risk of death for patients, and that general respiratory symptoms were also a strong predictor of death. Valentine cautions that the results are preliminary, and based on incomplete data that the team is currently working to fill.  One of the pandemic’s cruel realities is that it has hit the poorest and most vulnerable people in society hardest. Datathon participants also examined Covid-19’s social impact, from analyzing the impact of releasing prisoners to devising tools for people to verify the flood of claims about the disease now circulating online.  Amber Nigam, a data scientist based in New Delhi, India, has watched conspiracy theories spread and multiply on social media as contagiously as Covid-19 itself. “There’s a lot of anxiety,” he says. “Even my parents have shown me news on WhatsApp and asked if it was true.”  As the head of AI for PeopleStrong, a predictive sales startup in San Francisco, California, Nigam is comfortable with natural language processing tools and interested in their potential for fighting fake news. During the datathon, he and his team crawled the web for conspiracy theories circulating in the United States, China, and India, among other countries, and used the data to build an automated fact-checker. If the tool finds the claim to be untrue, it sends the reader to the news source where the claim was first debunked.  “A lot of people in rural settings don’t have access to accurate sources of information,” he says. “It’s super critical for people to have the right facts at their disposal.” Another team looked at Covid-19’s disparate impact on people of color. Lauren Chambers, a technology fellow at the Massachusetts American Civil Liberties Union (ACLU), suggested the project and mentored the team that took it on. State by state, the team found disproportionate death rates among Black and Hispanic people, who are more likely to work “essential” service-industry jobs where they face greater exposure to people infected with the disease. The gap was greatest in South Carolina, where Black individuals account for about half of Covid-19 deaths, but only a third of residents. The team noted that the picture nationally is probably worse, given that 10 states still do not collect race-specific data.  The team also found that poverty and lack of health care access were linked to higher death rates among Black communities, and language barriers were linked to higher death rates among Hispanic individuals. Their findings suggest that economic interventions for Black Americans, and hiring more hospital translators for Hispanic Americans, might be effective policies to reduce inequities in health outcomes. The ACLU can’t afford to hire an army of data scientists to investigate every civil-rights violation the pandemic has brought to light, says Chambers. But collaborative events like this one give community advocates a chance to explore urgent questions they wouldn’t otherwise be able to, she says, and data scientists get to hear new perspectives, too. “There’s a dangerous tendency among data scientists to think that numbers are the beginning and end of any good analysis,” she says. “But data are subjective, and there’s all kinds of other expertise that communities hold.” The event was sponsored by Beth Israel Deaconess Medical Center Innovation Group, Google Cloud, Massachusetts ACLU, and the National Science Foundation’s West Big Data Innovation Hub. A virtual “datathon” organized by MIT to bring fresh insights to the Covid-19 pandemic drew 300 participants and 44 mentors from around the world. Here, mentors who volunteered to judge the final projects meet on Zoom to select the top 10 projects. Image: Leo Anthony Celi https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 A global team of researchers searches for insights during a weeklong virtual “datathon.” Wed, 01 Jul 2020 11:20:01 -0400 https://news.mit.edu/2020/what-is-covid-19-data-tsunami-telling-policymakers-0701 Kim Martineau | MIT Quest for Intelligence Uncertainty about the course of the Covid-19 pandemic continues, with more than 2,500,000 known cases and 126,000 deaths in the United States alone. How to contain the virus, limit its damage, and address the deep-rooted health and racial inequalities it has exposed are now urgent topics for policymakers. Earlier this spring, 300 data scientists and health care professionals from around the world joined the MIT Covid-19 Datathon to see what insights they might uncover. “It felt important to be a part of,” says Ashley O’Donoghue, an economist at the Center for Healthcare Delivery Science at Beth Israel Deaconess Medical Center. “We thought we could produce something that might make a difference.” Participants were free to explore five tracks: the epidemiology of Covid-19, its policy impacts, its disparate health outcomes, the pandemic response in New York City, and the wave of misinformation Covid-19 has spawned. After splitting into teams, participants were set loose on 20 datasets, ranging from county-level Covid-19 cases compiled by The New York Times to a firehose of pandemic-related posts released by Twitter.  The participants, and the dozens of mentors who guided them, hailed from 44 countries and every continent except for Antarctica. To encourage the sharing of ideas and validation of results, the event organizers — MIT Critical Data, MIT Hacking Medicine, and the Martin Trust Center for MIT Entrepreneurship — required that all code be made available. In the end, 47 teams presented final projects, and 10 were singled out for recognition by a panel of judges. Several teams are now writing up their results for peer-reviewed publication, and at least one team has posted a paper. “It’s really hard to find research collaborators, especially during a crisis,” says Marie-Laure Charpignon, a PhD student with MIT’s Institute for Data, Systems, and Society, who co-organized the event. “We’re hoping that the teams and mentors that found each other will continue to explore these questions.” In a pre-print on medRxiv, O’Donoghue and her teammates identify the businesses most at risk for seeding new Covid-19 infections in New York, California, and New England. Analyzing location data from SafeGraph, a company that tracks commercial foot traffic, the team built a transmission-risk index for businesses that in the first five months of this year drew the most customers, for longer periods of time, and in more crowded conditions, due to their modest size.  Comparing this risk index to new weekly infections, the team classified 16.3 percent of countywide businesses as “superspreaders,” most of which were restaurants and hotels. A 1 percent increase in the density of super-spreader businesses, they found, was linked to a 5 percent jump in Covid-19 cases. The team is now extending its analysis to all 50 states, drilling down to ZIP code-level data, and building a decision-support tool to help several hospitals in their sample monitor risk as communities reopen. The tool will also let policymakers evaluate a wide range of statewide reopening policies. “If we see a second wave of infections, we can determine which policies actually worked,” says O’Donoghue. The datathon model for collaborative research is the brainchild of Leo Anthony Celi, a researcher at MIT and staff physician at Beth Israel Deaconess Medical Center. The events are usually coffee-fueled weekend affairs. But this one took place over a work week, and amid a global lockdown, with teammates having to meet and collaborate over Slack and Zoom. With no coffee breaks or meals, they had fewer chances to network, says Celi. But the virtual setting allowed more people to join, especially mentors, who could participate without taking time off to travel. It also may have made teams more efficient, he says.  After analyzing communication logs from the event, he and his colleagues found evidence that the most-successful teams lacked a clear leader. Everyone seemed to chip in. “In face-to-face events, leaders and followers emerge as they project their expertise and personalities,” he says. “But on Slack, we saw less hierarchy. The most successful teams showed high levels of enthusiasm and conversational turn-taking.” Another advantage of the virtual setting is that teams straddling several time zones could work, literally, around the clock. “You could post a message on Slack and someone would see it an hour or two later,” says Jane E. Valentine, a biomedical engineer at the Johns Hopkins University Applied Physics Laboratory. “There was a constant sense of engagement. I might be sleeping and doing nothing, but the wheels were still turning.” Valentine collaborated with a doctor and three data scientists in Europe, the United States, and Canada to analyze anonymized medical data from 4,000 Covid-19 patients to build predictive models for how long a new patient might need to be hospitalized, and their likelihood of dying. “It’s really useful for a clinician to know if a patient is likely to stabilize or go downhill,” she says. “You may want to monitor or treat them more aggressively.” Hospital administrators can also decide whether to open up additional wards, she adds. Among their findings, the team found that a fever and shortness of breath were top symptoms for predicting both a long hospital stay and a high risk of death for patients, and that general respiratory symptoms were also a strong predictor of death. Valentine cautions that the results are preliminary, and based on incomplete data that the team is currently working to fill.  One of the pandemic’s cruel realities is that it has hit the poorest and most vulnerable people in society hardest. Datathon participants also examined Covid-19’s social impact, from analyzing the impact of releasing prisoners to devising tools for people to verify the flood of claims about the disease now circulating online.  Amber Nigam, a data scientist based in New Delhi, India, has watched conspiracy theories spread and multiply on social media as contagiously as Covid-19 itself. “There’s a lot of anxiety,” he says. “Even my parents have shown me news on WhatsApp and asked if it was true.”  As the head of AI for PeopleStrong, a predictive sales startup in San Francisco, California, Nigam is comfortable with natural language processing tools and interested in their potential for fighting fake news. During the datathon, he and his team crawled the web for conspiracy theories circulating in the United States, China, and India, among other countries, and used the data to build an automated fact-checker. If the tool finds the claim to be untrue, it sends the reader to the news source where the claim was first debunked.  “A lot of people in rural settings don’t have access to accurate sources of information,” he says. “It’s super critical for people to have the right facts at their disposal.” Another team looked at Covid-19’s disparate impact on people of color. Lauren Chambers, a technology fellow at the Massachusetts American Civil Liberties Union (ACLU), suggested the project and mentored the team that took it on. State by state, the team found disproportionate death rates among Black and Hispanic people, who are more likely to work “essential” service-industry jobs where they face greater exposure to people infected with the disease. The gap was greatest in South Carolina, where Black individuals account for about half of Covid-19 deaths, but only a third of residents. The team noted that the picture nationally is probably worse, given that 10 states still do not collect race-specific data.  The team also found that poverty and lack of health care access were linked to higher death rates among Black communities, and language barriers were linked to higher death rates among Hispanic individuals. Their findings suggest that economic interventions for Black Americans, and hiring more hospital translators for Hispanic Americans, might be effective policies to reduce inequities in health outcomes. The ACLU can’t afford to hire an army of data scientists to investigate every civil-rights violation the pandemic has brought to light, says Chambers. But collaborative events like this one give community advocates a chance to explore urgent questions they wouldn’t otherwise be able to, she says, and data scientists get to hear new perspectives, too. “There’s a dangerous tendency among data scientists to think that numbers are the beginning and end of any good analysis,” she says. “But data are subjective, and there’s all kinds of other expertise that communities hold.” The event was sponsored by Beth Israel Deaconess Medical Center Innovation Group, Google Cloud, Massachusetts ACLU, and the National Science Foundation’s West Big Data Innovation Hub. A virtual “datathon” organized by MIT to bring fresh insights to the Covid-19 pandemic drew 300 participants and 44 mentors from around the world. Here, mentors who volunteered to judge the final projects meet on Zoom to select the top 10 projects. Image: Leo Anthony Celi https://news.mit.edu/2020/ali-jadbabaie-named-mit-department-civil-environmental-engineering-head-0626 Systems science and control theory expert to succeed Markus Buehler. Fri, 26 Jun 2020 14:40:31 -0400 https://news.mit.edu/2020/ali-jadbabaie-named-mit-department-civil-environmental-engineering-head-0626 Lori LoTurco | School of Engineering Ali Jadbabaie, the JR East Professor of Engineering, has been named the new head of the Department of Civil and Environmental Engineering (CEE), effective Sept. 1.“Ali’s work has crossed disciplines and departments and led to multi-university collaborations,” says Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science, in an announcement to the CEE community. “He has made outstanding contributions as an educator, in addition to serving as a leader in MIT’s multi-disciplinary efforts — particularly in understanding the dynamics of social networks, spreading processes, collective behavior, and collective decision-making in socio-technical systems. He will undoubtedly be a remarkable next leader for CEE.”Jadbabaie succeeds Markus Buehler, the McAfee Professor of Engineering, who has led CEE since 2013. “I am grateful to Markus for his leadership, dedication, and contributions to helping shape CEE across the past seven years,” says Chandrakasan.Currently, Jadbabaie also serves as associate director of the Institute for Data, Systems, and Society (IDSS), director of the Sociotechnical Systems Research Center, and a principal investigator in the Laboratory for Information and Decision Systems (LIDS). He has made fundamental contributions in optimization-based control, multi-agent coordination and consensus, collective decision-making, network science, and network economics.  Jadbabaie graduated from Sharif University of Technology with a BS in electrical engineering (with a focus on control systems), and went on to receive his MS in electrical and computer engineering from the University of New Mexico, and his PhD in control and dynamical systems from Caltech. His work as a postdoc at Yale University set him on career path in network science and multi-agent coordination and control.Jadbabaie spent 14 years at the University of Pennsylvania, where he held the Alfred Fitler Moore Professorship of Network Sciences in the Department of Electrical and Systems Engineering. He also held secondary appointments in the Department of Computer and Information Science as well as the Department of Operations, Information and Decisions in the Wharton School. In 2014, Ali was recruited to join MIT as a visiting professor, to help lay the groundwork for the new IDSS, which included the establishment of its flagship doctoral program in Social and Engineering Systems (SES). He also served as interim director of the Sociotechnical Systems Research Center. In 2016, he formally joined the MIT faculty with a joint appointment in the Department of Civil and Environmental Engineering and the IDSS.In recognition of his work on multi-agent coordination and control and network science, Ali was named an IEEE Fellow; he also served as the inaugural editor-in-chief of IEEE Transactions on Network Science and Engineering, an interdisciplinary journal sponsored by several IEEE societies. He is a 2016 recipient of a Vannevar Bush Fellowship from the Office of the Secretary of Defense, in addition to being the recipient of a National Science Foundation Career Award, an Office of Naval Research Young Investigator Award, the O. Hugo Schuck Best Paper Award from the American Automatic Control Council, and the George S. Axelby Best Paper Award from the IEEE Control Systems Society. Several of his student advisees have also won best paper awards. Ali Jadbabaie has been named the new head of the MIT Department of Civil and Environmental Engineering. https://news.mit.edu/2020/macro-eyes-vaccine-chain-health-equity-0626 The startup macro-eyes uses artificial intelligence to improve vaccine delivery and patient scheduling. Thu, 25 Jun 2020 23:59:59 -0400 https://news.mit.edu/2020/macro-eyes-vaccine-chain-health-equity-0626 Zach Winn | MIT News Office More children are being vaccinated around the world today than ever before, and the prevalence of many vaccine-preventable diseases has dropped over the last decade. Despite these encouraging signs, however, the availability of essential vaccines has stagnated globally in recent years, according the World Health Organization.One problem, particularly in low-resource settings, is the difficulty of predicting how many children will show up for vaccinations at each health clinic. This leads to vaccine shortages, leaving children without critical immunizations, or to surpluses that can’t be used.The startup macro-eyes is seeking to solve that problem with a vaccine forecasting tool that leverages a unique combination of real-time data sources, including new insights from front-line health workers. The company says the tool, named the Connected Health AI Network (CHAIN), was able to reduce vaccine wastage by 96 percent across three regions of Tanzania. Now it is working to scale that success across Tanzania and Mozambique.“Health care is complex, and to be invited to the table, you need to deal with missing data,” says macro-eyes Chief Executive Officer Benjamin Fels, who co-founded the company with Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor at MIT. “If your system needs age, gender, and weight to make predictions, but for one population you don’t have weight or age, you can’t just say, ‘This system doesn’t work.’ Our feeling is it has to be able to work in any setting.”The company’s approach to prediction is already the basis for another product, the patient scheduling platform Sibyl, which has analyzed over 6 million hospital appointments and reduced wait times by more than 75 percent at one of the largest heart hospitals in the U.S. Sibyl’s predictions work as part of CHAIN’s broader forecasts.Both products represent steps toward macro-eyes’ larger goal of transforming health care through artificial intelligence. And by getting their solutions to work in the regions with the least amount of data, they’re also advancing the field of AI.“The state of the art in machine learning will result from confronting fundamental challenges in the most difficult environments in the world,” Fels says. “Engage where the problems are hardest, and AI too will benefit: [It will become] smarter, faster, cheaper, and more resilient.”Defining an approachSra and Fels first met about 10 years ago when Fels was working as an algorithmic trader for a hedge fund and Sra was a visiting faculty member at the University of California at Berkeley. The pair’s experience crunching numbers in different industries alerted them to a shortcoming in health care. “A question that became an obsession to me was, ‘Why were financial markets almost entirely determined by machines — by algorithms — and health care the world over is probably the least algorithmic part of anybody’s life?’” Fels recalls. “Why is health care not more data-driven?” Around 2013, the co-founders began building machine-learning algorithms that measured similarities between patients to better inform treatment plans at Stanford School of Medicine and another large academic medical center in New York. It was during that early work that the founders laid the foundation of the company’s approach.“There are themes we established at Stanford that remain today,” Fels says. “One is [building systems with] humans in the loop: We’re not just learning from the data, we’re also learning from the experts. The other is multidimensionality. We’re not just looking at one type of data; we’re looking at 10 or 15 types, [including] images, time series, information about medication, dosage, financial information, how much it costs the patient or hospital.”Around the time the founders began working with Stanford, Sra joined MIT’s Laboratory for Information and Decision Systems (LIDS) as a principal research scientist. He would go on to become a faculty member in the Department of Electrical Engineering and Computer Science and MIT’s Institute for Data, Systems, and Society (IDSS). The mission of IDSS, to advance fields including data science and to use those advances to improve society, aligned well with Sra’s mission at macro-eyes.“Because of that focus [on impact] within IDSS, I find it my focus to try to do AI for social good,’ Sra says. “The true judgment of success is how many people did we help? How could we improve access to care for people, wherever they may be?” In 2017, macro-eyes received a small grant from the Bill and Melinda Gates Foundation to explore the possibility of using data from front-line health workers to build a predictive supply chain for vaccines. It was the beginning of a relationship with the Gates Foundation that has steadily expanded as the company has reached new milestones, from building accurate vaccine utilization models in Tanzania and Mozambique to integrating with supply chains to make vaccine supplies more proactive. To help with the latter mission, Prashant Yadav recently joined the board of directors; Yadav worked as a professor of supply chain management with the MIT-Zaragoza International Logistics Program for seven years and is now a senior fellow at the Center for Global Development, a nonprofit thinktank. In conjunction with their work on CHAIN, the company has deployed another product, Sibyl, which uses machine learning to determine when patients are most likely to show up for appointments, to help front-desk workers at health clinics build schedules. Fels says the system has allowed hospitals to improve the efficiency of their operations so much they’ve reduced the average time patients wait to see a doctor from 55 days to 13 days. As a part of CHAIN, Sibyl similarly uses a range of data points to optimize schedules, allowing it to accurately predict behavior in environments where other machine learning models might struggle. The founders are also exploring ways to apply that approach to help direct Covid-19 patients to health clinics with sufficient capacity. That work is being developed with Sierra Leone Chief Innovation Officer David Sengeh SM ’12 PhD ’16. Pushing frontiers Building solutions for some of the most underdeveloped health care systems in the world might seem like a difficult way for a young company to establish itself, but the approach is an extension of macro-eyes’ founding mission of building health care solutions that can benefit people around the world equally.“As an organization, we can never assume data will be waiting for us,” Fels says. “We’ve learned that we need to think strategically and be thoughtful about how to access or generate the data we need to fulfill our mandate: Make the delivery of health care predictive, everywhere.”The approach is also a good way to explore innovations in mathematical fields the founders have spent their careers working in.“Necessity is absolutely the mother of invention,” Sra says. “This is innovation driven by need.”And going forward, the company’s work in difficult environments should only make scaling easier.“We think every day about how to make our technology more rapidly deployable, more generalizable, more highly scalable,” Sra says. “How do we get to the immense power of bringing true machine learning to the world’s most important problems without first spending decades and billions of dollars in building digital infrastructure? How do we leap into the future?” The startup macro-eyes is bringing new techniques in machine learning and artificial intelligence to global health problems like vaccine delivery and patient scheduling with its Connected Health AI Network (CHAIN). Courtesy of macro-eyes https://news.mit.edu/2020/macro-eyes-vaccine-chain-health-equity-0626 The startup macro-eyes uses artificial intelligence to improve vaccine delivery and patient scheduling. Thu, 25 Jun 2020 23:59:59 -0400 https://news.mit.edu/2020/macro-eyes-vaccine-chain-health-equity-0626 Zach Winn | MIT News Office More children are being vaccinated around the world today than ever before, and the prevalence of many vaccine-preventable diseases has dropped over the last decade. Despite these encouraging signs, however, the availability of essential vaccines has stagnated globally in recent years, according the World Health Organization.One problem, particularly in low-resource settings, is the difficulty of predicting how many children will show up for vaccinations at each health clinic. This leads to vaccine shortages, leaving children without critical immunizations, or to surpluses that can’t be used.The startup macro-eyes is seeking to solve that problem with a vaccine forecasting tool that leverages a unique combination of real-time data sources, including new insights from front-line health workers. The company says the tool, named the Connected Health AI Network (CHAIN), was able to reduce vaccine wastage by 96 percent across three regions of Tanzania. Now it is working to scale that success across Tanzania and Mozambique.“Health care is complex, and to be invited to the table, you need to deal with missing data,” says macro-eyes Chief Executive Officer Benjamin Fels, who co-founded the company with Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor at MIT. “If your system needs age, gender, and weight to make predictions, but for one population you don’t have weight or age, you can’t just say, ‘This system doesn’t work.’ Our feeling is it has to be able to work in any setting.”The company’s approach to prediction is already the basis for another product, the patient scheduling platform Sibyl, which has analyzed over 6 million hospital appointments and reduced wait times by more than 75 percent at one of the largest heart hospitals in the U.S. Sibyl’s predictions work as part of CHAIN’s broader forecasts.Both products represent steps toward macro-eyes’ larger goal of transforming health care through artificial intelligence. And by getting their solutions to work in the regions with the least amount of data, they’re also advancing the field of AI.“The state of the art in machine learning will result from confronting fundamental challenges in the most difficult environments in the world,” Fels says. “Engage where the problems are hardest, and AI too will benefit: [It will become] smarter, faster, cheaper, and more resilient.”Defining an approachSra and Fels first met about 10 years ago when Fels was working as an algorithmic trader for a hedge fund and Sra was a visiting faculty member at the University of California at Berkeley. The pair’s experience crunching numbers in different industries alerted them to a shortcoming in health care. “A question that became an obsession to me was, ‘Why were financial markets almost entirely determined by machines — by algorithms — and health care the world over is probably the least algorithmic part of anybody’s life?’” Fels recalls. “Why is health care not more data-driven?” Around 2013, the co-founders began building machine-learning algorithms that measured similarities between patients to better inform treatment plans at Stanford School of Medicine and another large academic medical center in New York. It was during that early work that the founders laid the foundation of the company’s approach.“There are themes we established at Stanford that remain today,” Fels says. “One is [building systems with] humans in the loop: We’re not just learning from the data, we’re also learning from the experts. The other is multidimensionality. We’re not just looking at one type of data; we’re looking at 10 or 15 types, [including] images, time series, information about medication, dosage, financial information, how much it costs the patient or hospital.”Around the time the founders began working with Stanford, Sra joined MIT’s Laboratory for Information and Decision Systems (LIDS) as a principal research scientist. He would go on to become a faculty member in the Department of Electrical Engineering and Computer Science and MIT’s Institute for Data, Systems, and Society (IDSS). The mission of IDSS, to advance fields including data science and to use those advances to improve society, aligned well with Sra’s mission at macro-eyes.“Because of that focus [on impact] within IDSS, I find it my focus to try to do AI for social good,’ Sra says. “The true judgment of success is how many people did we help? How could we improve access to care for people, wherever they may be?” In 2017, macro-eyes received a small grant from the Bill and Melinda Gates Foundation to explore the possibility of using data from front-line health workers to build a predictive supply chain for vaccines. It was the beginning of a relationship with the Gates Foundation that has steadily expanded as the company has reached new milestones, from building accurate vaccine utilization models in Tanzania and Mozambique to integrating with supply chains to make vaccine supplies more proactive. To help with the latter mission, Prashant Yadav recently joined the board of directors; Yadav worked as a professor of supply chain management with the MIT-Zaragoza International Logistics Program for seven years and is now a senior fellow at the Center for Global Development, a nonprofit thinktank. In conjunction with their work on CHAIN, the company has deployed another product, Sibyl, which uses machine learning to determine when patients are most likely to show up for appointments, to help front-desk workers at health clinics build schedules. Fels says the system has allowed hospitals to improve the efficiency of their operations so much they’ve reduced the average time patients wait to see a doctor from 55 days to 13 days. As a part of CHAIN, Sibyl similarly uses a range of data points to optimize schedules, allowing it to accurately predict behavior in environments where other machine learning models might struggle. The founders are also exploring ways to apply that approach to help direct Covid-19 patients to health clinics with sufficient capacity. That work is being developed with Sierra Leone Chief Innovation Officer David Sengeh SM ’12 PhD ’16. Pushing frontiers Building solutions for some of the most underdeveloped health care systems in the world might seem like a difficult way for a young company to establish itself, but the approach is an extension of macro-eyes’ founding mission of building health care solutions that can benefit people around the world equally.“As an organization, we can never assume data will be waiting for us,” Fels says. “We’ve learned that we need to think strategically and be thoughtful about how to access or generate the data we need to fulfill our mandate: Make the delivery of health care predictive, everywhere.”The approach is also a good way to explore innovations in mathematical fields the founders have spent their careers working in.“Necessity is absolutely the mother of invention,” Sra says. “This is innovation driven by need.”And going forward, the company’s work in difficult environments should only make scaling easier.“We think every day about how to make our technology more rapidly deployable, more generalizable, more highly scalable,” Sra says. “How do we get to the immense power of bringing true machine learning to the world’s most important problems without first spending decades and billions of dollars in building digital infrastructure? How do we leap into the future?” The startup macro-eyes is bringing new techniques in machine learning and artificial intelligence to global health problems like vaccine delivery and patient scheduling with its Connected Health AI Network (CHAIN). Courtesy of macro-eyes https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619 The startup ClosedLoop has created a platform of predictive models to help organizations improve patient care. Thu, 18 Jun 2020 23:59:59 -0400 https://news.mit.edu/2020/closedloop-ai-predictive-health-care-0619 Zach Winn | MIT News Office An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital — before they to return to the doctor’s office with further complications.But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that aren’t optimized for their patients.Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.The platform is being used to help hospitals determine which patients are most likely to miss appointments, acquire infections like sepsis, benefit from periodic check ups, and more. Health insurers, in turn, are using ClosedLoop to make population-level predictions around things like patient readmissions and the onset or progression of chronic diseases.“We built a health care data science platform that can take in whatever data an organization has, quickly build models that are specific to [their patients], and deploy those models,” says ClosedLoop co-founder and Chief Technology Officer Dave DeCaprio ’94. “Being able to take somebody’s data the way it lives in their system and convert that into a model that can be readily used is still a problem that requires a lot of [health care] domain knowledge, and that’s a lot of what we bring to the table.”In light of the Covid-19 pandemic, ClosedLoop has also created a model that helps organizations identify the most vulnerable people in their region and prepare for patient surges. The open source tool, called the C-19 Index, has been used to connect high-risk patients with local resources and helped health care systems create risk scores for tens of millions of people overall.The index is just the latest way that ClosedLoop is accelerating the health care industry’s adoption of AI to improve patient health, a goal DeCaprio has worked toward for the better part of his career.Designing a strategyAfter working as a software engineer for several private companies through the internet boom of the early 2000s, DeCaprio was looking to make a career change when he came across a project focused on genome annotation at the Broad Institute of MIT and Harvard.The project was DeCaprio’s first professional exposure to the power of artificial intelligence. It blossomed into a six year stint at the Broad, after which he continued exploring the intersection of big data and health care.“After a year in health care, I realized it was going to be really hard to do anything else,” DeCaprio says. “I’m not going to be able to get excited about selling ads on the internet or anything like that. Once you start dealing with human health, that other stuff just feels insignificant.”In the course of his work, DeCaprio began noticing problems with the ways machine learning and other statistical techniques were making their way into health care, notably in the fact that predictive models were being applied without regard for hospitals’ patient populations.“Someone would say, ‘I know how to predict diabetes’ or ‘I know how to predict readmissions,’ and they’d sell a model,” DeCaprio says. “I knew that wasn’t going to work, because the reason readmissions happen in a low-income population of New York City is very different from the reason readmissions happen in a retirement community in Florida. The important thing wasn’t to build one magic model but to build a system that can quickly take somebody’s data and train a model that’s specific for their problems.”With that approach in mind, DeCaprio joined forces with former co-worker and serial entrepreneur Andrew Eye, and started ClosedLoop in 2017. The startup’s first project involved creating models that predicted patient health outcomes for the Medical Home Network (MHN), a not-for-profit hospital collaboration focused on improving care for Medicaid recipients in Chicago.As the founders created their modeling platform, they had to address many of the most common obstacles that have slowed health care’s adoption of AI solutions.Often the first problems startups run into is making their algorithms work with each health care system’s data. Hospitals vary in the type of data they collect on patients and the way they store that information in their system. Hospitals even store the same types of data in vastly different ways.DeCaprio credits his team’s knowledge of the health care space with helping them craft a solution that allows customers to upload raw data sets into ClosedLoop’s platform and create things like patient risk scores with a few clicks.Another limitation of AI in health care has been the difficulty of understanding how models get to results. With ClosedLoop’s models, users can see the biggest factors contributing to each prediction, giving them more confidence in each output.Overall, to become ingrained in customer’s operations, the founders knew their analytics platform needed to give simple, actionable insights. That has translated into a system that generates lists, risk scores, and rankings that care managers can use when deciding which interventions are most urgent for which patients.“When someone walks into the hospital, it’s already too late [to avoid costly treatments] in many cases,” DeCaprio says. “Most of your best opportunities to lower the cost of care come by keeping them out of the hospital in the first place.”Customers like health insurers also use ClosedLoop’s platform to predict broader trends in disease risk, emergency room over-utilization, and fraud.Stepping up for Covid-19In March, ClosedLoop began exploring ways its platform could help hospitals prepare for and respond to Covid-19. The efforts culminated in a company hackathon over the weekend of March 16. By Monday, ClosedLoop had an open source model on GitHub that assigned Covid-19 risk scores to Medicare patients. By that Friday, it had been used to make predictions on more than 2 million patients.Today, the model works with all patients, not just those on Medicare, and it has been used to assess the vulnerability of communities around the country. Care organizations have used the model to project patient surges and help individuals at the highest risk understand what they can do to prevent infection.“Some of it is just reaching out to people who are socially isolated to see if there’s something they can do,” DeCaprio says. “Someone who is 85 years old and shut in may not know there’s a community based organization that will deliver them groceries.”For DeCaprio, bringing the predictive power of AI to health care has been a rewarding, if humbling, experience.“The magnitude of the problems are so large that no matter what impact you have, you don’t feel like you’ve moved the needle enough,” he says. “At the same time, every time an organization says, ‘This is the primary tool our care managers have been using to figure out who to reach out to,’ it feels great.” The startup ClosedLoop.ai, co-founded by an MIT alumnus, is using a platform of AI models to help hospitals make predictions based on their patient data. Image: MIT News, with images courtesy of the researchers https://news.mit.edu/2020/delayed-deceleration-zooming-jacqueline-thomas-mit-0614 Jacqueline Thomas PhD ’20 recounts her final academic year at MIT, from once-in-a-lifetime field work to a virtual thesis defense. Sun, 14 Jun 2020 00:00:00 -0400 https://news.mit.edu/2020/delayed-deceleration-zooming-jacqueline-thomas-mit-0614 Sara Cody | Department of Aeronautics and Astronautics On Nov. 21, 2019, the sun had set just a couple of hours before on an unseasonably warm day, and Jacqueline Thomas PhD ’20 found herself sitting on the edge of her seat in a typical meeting room in the William J. Hughes Technical Center, part of the Federal Aviation Administration, in Atlantic City, New Jersey. Thomas, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro) at MIT, focused intently in front of a small monitor, her eyes fixed on the black screen illuminated by a white outline of the U.S. East Coast and the small, neon green dot that showed the Boeing 777 commercial airplane, which had flown nearly nine hours from Frankfurt, Germany, and was just about to land at Atlantic City International Airport. The last three minutes of this flight were crucial, and it was exactly the moment Thomas had been waiting for. Accompanied by her advisor, R. John Hansman, professor of aeronautics and director of the MIT International Center for Air Transportation, Thomas felt her heart pounding as she monitored the data the plane generated as it landed in real time, which she checked simultaneously against her predicted outcomes based on her computational model. These final moments of this particular aircraft’s journey would determine if the model that formed a significant portion of her graduate thesis worked in the real world. And it did. “We waited all day for these final three minutes, and as we watched the plane land through the monitor, my advisor John kept asking if the plane was doing what I expected it to, and it was! Even though I predicted it, it was still surprising,” says Thomas. “I knew the science was sound, I knew the math was sound, but even when everything is going as planned and you are actually seeing it happening with your own eyes, it’s still surreal.” Just a few short months earlier, Thomas proposed her idea for a “delayed deceleration approach” to Boeing under their ecoDemonstrator (ecoD) program. Essentially, the Boeing ecoD acts as a “bench-to-bedside” innovation accelerator, inviting researchers to pitch novel concepts to improve aviation safety and efficiency that solve real-world challenges for aviation and the environment, where they are tested in real aircraft to demonstrate feasibility. Thomas’ proposal outlined a new flight procedure for pilots to follow while landing that improves aircraft performance around two major challenges the airline is currently facing: carbon emissions and noise pollution. According to a report released in October 2019 by the Environmental Protection Agency, air travel currently accounts for nearly 2.5 percent of global carbon dioxide emissions, and it is increasing at a much faster rate than initially anticipated. In addition to the negative environmental impact, the increase in the number of commercial flights has increased the number of noise complaints from citizens who live along flight trajectories beyond the jurisdiction of noise regulations, which are typically localized to the areas immediately surrounding airports. The pressure is on for airline companies to work quickly to address these issues, and Thomas proposed a concept that decreased the noise and emissions of existing aircraft without having to modify the aircraft itself, which could be a cost-effective way for airlines to mitigate these issues. “As soon as a plane is built, it’s hard to change its function. It will generate noise no matter what state it’s in,” says Thomas. “I chose to approach the problem like an integrated system — if you can change the input, you can change the output. In other words, if you can’t change the aircraft itself (the function), you can change how it’s flown (the inputs).” Using this idea, Thomas built a computational framework to analyze aircraft noise and measure the impact of making changes to the operational flight procedure. For her analysis, the inputs included how all of the aircraft components move and interact to generate noise, as well as flight performance data, which accounts for how the aircraft generates noise at different points as it moves through its environment, such as when it accelerates or slows down. The output from this framework was a full-scope overflight noise model, which was then analyzed against community data to paint a clear picture of how making tweaks to the inputs would impact the noise pollution in surrounding communities. “What resulted from this framework was my concept for the delayed deceleration approach, a new flight procedure where the aircraft remains cleanly configured for as long as possible during approach, meaning the flaps, slats, and landing gear remain upright for as long as possible,” says Thomas. “When the aircraft has a clean configuration, it is more aerodynamic, creating less drag and allowing it to maintain engines at a lower power setting for longer duration in the flight. As a result, the plane burns less fuel, decreasing carbon emissions, and generates less noise for the community on the ground.” Under the ecoD program, Thomas handed her procedure over to Boeing engineers in Seattle, Washington, who communicated it to the crew throughout the flight via a chat feed that Thomas and Hansman could see on the monitor, along with the plane’s location. Immediately following the landing, the all-women flight crew joined Thomas, Hansman, and the group of Boeing engineers and administrators from the ecoD program for a debrief. “The pilots reported they felt very comfortable with the procedure, and didn’t experience any flyability issues. When the models say that it works and has all of these benefits, and the pilots say ‘yes, we can fly this,’ and a commercial plane actually flies the procedure and matches the predictions from the models, then it really shows that we can do this, and we should because it’s a win-win for everyone,” says Thomas. “My goal for the future is to make this a standard flight procedure, which means I need to keep working on refining this process so we can scale it up in a way that makes sense to implement in real airlines operating today.” After nearly six years and countless hours spent at the computer in the lab, this was an extraordinary opportunity for a graduate student; it can take years to put together a flight test, and thanks to the Boeing ecoD program, this test came together in a matter of a few months. It was the perfect way to begin winding down her final year at MIT. With the excitement of the ecoDemonstrator behind her, Thomas set her sights on preparing for one of the biggest milestones in a graduate student’s career: the thesis defense. Typically, this rite of passage is a celebratory one that comes after months of coordinating busy thesis committee schedules and practicing presentations backward and forward. Thomas was also in the process of job hunting, interviewing for academic positions in between putting the finishing touches on her thesis presentation. And then the coronavirus hit. As the pandemic and MIT’s response to it rapidly unfolded, campus closures, travel restrictions, and stay-at-home orders snapped the public health crisis into focus. Everything became a scramble as Thomas watched months of planning go out the window, and she knew she would have to improvise quickly. “I had to move my defense online, and my internet at home is really sketchy, so I was terrified,” says Thomas. “It was weird not worrying about the typical things you would normally worry about before a thesis defense, like wondering if my presentation was good enough. I was more nervous about needing to defend my thesis by holding my phone up to my face.” Thomas submitted a formal request to MIT to use one of the few classrooms that remained open on campus by appointment only to defend her thesis. Instead of defending her thesis to a room full of people, she was in an empty room on a Zoom call, where she could only see five attendees at any given time. When she finished her presentation and answered all of the questions from her thesis committee, she was asked to log off of the Zoom call, where she sat in silence in the cavernous room, alone. Five minutes later, she received a congratulatory phone call, and just like that, she was a doctor. “It was bizarre. Normally you are with other people to talk to and celebrate with, but I was just in a room by myself, and there was no one else at MIT,” says Thomas. “One of the cool things about holding my defense virtually was that my friend in Japan logged in to watch, even though it was 2 a.m. his time. But my fiancé, who is also studying aerospace as a grad student at Georgia Tech, wanted to come be with me for my defense, but we decided together that with the safety measures asking visitors from out-of-state to self-quarantine, it just wasn’t possible.” Thomas, like many graduate students, lived in an off-campus apartment with a roommate, a postdoc at a neighboring university, who had only recently moved in. Since graduate students and postdocs spend so much time on campus, this is a typical living arrangement. Many graduate students attend school far from home, so the stay-at-home order can be particularly isolating, especially when you are living with a near-perfect stranger without work to focus on. Since turning in her thesis, Thomas kept busy with early-morning runs around the Charles River, refreshing her Japanese and Spanish-speaking vocabulary, catching up on TV shows she’d fallen behind on while dealing with the demands of graduate school, and trying to maintain glimmers of normalcy, such as attending regular church services (albeit virtually). While exciting career opportunities are on the horizon, many other personal plans, like her wedding date, are at a standstill as we remain in the grip of uncertainty at the mercy of a global pandemic. “It feels like I’m in a limbo state, because my work is pretty much done and I’m just waiting for the next chapter to start, which feels like it’s taking longer than usual because so much of it is spent alone,” says Thomas. For Thomas, one of the more difficult aspects she is grappling with is the abrupt ending of her time at MIT. As a first-generation college student, Thomas’ family had set aside money for the major expense of traveling to experience MIT Commencement with her, and it was tough to watch her families’ travel plans, and the hard-earned money put toward them, evaporate. “Grad school is hard, but looking back, you realize how much you grew throughout the experience, and I wanted to tip my hat to MIT when I left,” says Thomas. “This is my sixth year here, and it’s a long time to be involved at a place and then suddenly, it leaves you within three days. I think the hardest thing for me has been this lack of closure. It’s like a severed connection.” Thomas is hopeful for the future. She will become a member of the faculty at her alma mater, the University of California at Irvine, where she will teach and continue her work on aircraft noise mitigation and pursue exciting new directions studying electric aircraft. She also hopes for future events that could bring the Class of 2020 back on campus to say a proper goodbye — once it is safe to do so. “I knew the science was sound, I knew the math was sound, but even when everything is going as planned and you are actually seeing it happening with your own eyes, it’s still surreal,” says Jacqueline Thomas PhD ’20 on watching a Boeing 777 commercial airplane land using an approach she designed as an MIT grad student. Photo: Jacqueline Thomas https://news.mit.edu/2020/social-life-of-data-0608 New Data and Society course engages students in the ethics and societal implications of data. Mon, 08 Jun 2020 15:00:01 -0400 https://news.mit.edu/2020/social-life-of-data-0608 School of Humanities, Arts, and Social Sciences On a typical day in our data-saturated world, Facebook announces plans to encrypt its Messenger data, prompting uproar from child welfare activists who fear privacy will come at the cost of online safety. A new company called Tillable, an AirBnB for farmers, makes headlines for allowing the public to rent farmland while collecting and tracking massive swathes of data on land use and profitability. Tesla comes under fire for concealing autopilot data, while the U.S. Federal Trade Commission announces that 2019 was a record year in protecting consumer privacy. Given the daily avalanche of news in the contemporary tug of war between privacy and safety, Data and Society (STS 11.155J/STS.005J) always begins with a discussion of current events. One of 36 classes in the new Computing and Society concentration in MIT’s School of Humanities, Arts, and Social Sciences, Data and Society focuses on two linked concepts: the process of data creation and analysis, and the ethical quandaries and policy vacuums surrounding how those data impact society. A gestalt approach to data “The purpose of this class is to engage MIT students in thinking about data — data creation, data analysis — in ways that are not only technical but are also societal,” says Eden Medina, associate professor of science, technology, and society, who co-taught the class this spring with Sarah Williams, an associate professor of technology and urban planning. Medina is particularly well-versed in the social, historical, and ethical aspects of computing, and Williams brings expertise as a practicing data scientist. Their multi-layered course is designed to “train practitioners who think about the ethics of the work that they’re doing” and who know how to use data in responsible ways. Medina and Williams crafted the inaugural semester of Data and Society around the life-cycle stages of a normal data science project, guiding students to consider project facts such as who is collecting the data, how is the data created, and how it is analyzed. Students then explore broader questions, including: How can power intersect with the way those data are created? What is the role of bias in data creation? What is informed consent and what role might it play into the way that datasets are generated and then eventually used and reused?   Impacts of data collection in daily life As the course continues, students begin to discover the fine threads of cause and effect that can often slip under a purely technical radar. Bias in data collection, for instance, can have subtle and insidious effects on how the world is constructed around us; for instance, the way in which data are collected could further pre-existing bias rooted in social inequality. Practices of data collection, aggregation, and reuse can also present challenges for ethical practices such as informed consent. How can we make an informed decision without fully understanding how our data might be used in the future and the ramifications of that use?   “I have worked a lot on the technical side with data both in my computer science classes, and with work experiences and my UROP [undergraduate research project],” says Darian Bhathena ’20, a recent graduate whose studies span computer science and engineering, biomedical engineering, and urban studies and planning. “As engineering students, we sometimes forget that, to be useful and applicable, all the technical material we’re learning has to fit within society as a whole.” The intricate impacts of data collection in the students’ daily lives — from what they see in their Twitter feeds to how they interact with health-tracking apps — are front and center in the class, making the curriculum material and its implications personal. A challenge at the core of a data-driven society For one assignment, students created visualizations from data they collected, endeavoring to be as neutral as possible, then wrote about the decisions they made, including non-technical decisions, to build the dataset and use it for analysis. One student downloaded all her text messages for a week, trying to track a correlation between weather and texting patterns. Another tried to determine which MIT dorm was the healthiest, entering diet data into a program they designed. Another student tried to track her own water usage against self-reported norms across the Cambridge, Massachusetts, area. All of the students ran into assumptions in their data models — for instance, about how much water is used to wash hands, or how diets change over time. One by one, the students faced a series of built-in human decisions that prevented their data from being truly neutral. The exercise illustrated the challenge at the core of our data-driven society: data are easy to gather, but their implications are far less easy to discern and manage. “A lot of decisions around data in the world are ours to make,” says Williams. “Technology moves much more quickly than regulation can.”   Fluency in the ethics of technology The new Computing and Society concentration, of which Data and Society is a core course, is part of a larger push across the Institute, echoed in the mission of the new MIT Schwarzman College of Computing, to enable a holistic view of how technology both shapes, and is shaped by, the nuances of the world, and to develop Institute-wide fluency in the ethics of technology.    Zach Johnson, a rising junior majoring in computer science and engineering, is also pursuing the new Computing and Society concentration. He says his experience in simultaneous technical and humanistic instruction has been eye-opening. “I get to see all the application of what I am learning in the real world and get to learn the ethics behind what I am doing,” he explains. “While I am learning how to write the code in my Course 6 classes, this class is showing me how that code is used to do incredible good or incredible harm for the world.” In the current public health crisis, Johnson is eager to apply his new insights to this unprecedented moment in the course’s final project. The assignment: study how another country is using data to address the coronavirus pandemic and identify which aspects of this approach, if any, the United States should adopt. Johnson says, “While all the topics of this course are interesting, it is particularly fascinating to be able to apply what is happening in the world during a time of crisis to my study of data science.” Does tech provide more objective decisions? Medina, herself a 2005 doctoral graduate of the MIT STS program, joined the faculty last July. Her current research centers on technology and human rights, with a focus on Chile. Much of her previous and current scholarship relates to how people use data to bring certainty to highly uncertain situations, and how our increased trust in technology and its capabilities echo through social realities. “I see [this research] as very relevant to emerging issues in artificial intelligence and machine learning — because we are now putting our faith in new technological systems that are built on large repositories of data and whose decision-making processes are often not transparent. We are trusting them to give us a more objective decision — often without having the means to consider how flawed that ‘objective’ decision might be. What harms can result from such practices?” Williams’ Civic Data Design Lab is immersed in questions of how data can be used to expose and inform urban policies. In one example from her book, “Data Action,” she created a model to identify cities in China that were built but never inhabited. The model was based on the idea that thriving communities need amenities (grocery stores and schools) — analysis of Chinese social media data showed that in many Chinese cities these basic resources did not exist, and therefore they were “ghost cities.” Williams lab went further to visualize the data to “ground truth” the results with Chinese officials. The approach allowed more candid conversations with the government and a more accurate model for understanding the phenomenon of China’s vacant cities. “We hear a lot about how data can be used for bad things, which is true, but it also can be used for good,” reflects Williams. “Like anything in the world, data is a tool, and that tool can be used to improve society, rather than cause harm.” Based on the inaugural class, Williams thinks Data and Society is exactly the kind of rigorous, thoughtful environment that will empower MIT graduates, helping them develop the awareness, analytical/ethical framework, and skills needed to act consciously as data practitioners in the field. “Engaging students across disciplines — that’s how innovation happens,” she says. Story prepared by MIT SHASS Communications Editorial and Design Director: Emily Hiestand Writer: Alison Lanier, Senior Communications Associate Left to right: MIT associate professors Eden Medina of science, technology, and society and Sarah Williams of technology and urban planning. Medina is particularly well-versed in the social, historical, and ethical aspects of computing, and Williams brings expertise as a practicing data scientist. Their multi-layered course, Data and Society, is designed to train practitioners who know how to use data in responsible ways. Photos: Allegra Boverman (left) and Department of Urban Studies and Planning (right) https://news.mit.edu/2020/mit-csail-computing-technology-after-moores-law-0605 MIT CSAIL researchers say improving computing technology after Moore's Law will require more efficient software, new algorithms, and specialized hardware. Fri, 05 Jun 2020 11:50:01 -0400 https://news.mit.edu/2020/mit-csail-computing-technology-after-moores-law-0605 Adam Conner-Simons | MIT CSAIL In 1965, Intel co-founder Gordon Moore predicted that the number of transistors that could fit on a computer chip would grow exponentially — and they did, doubling about every two years. For half a century, Moore’s Law has endured: Computers have gotten smaller, faster, cheaper, and more efficient, enabling the rapid worldwide adoption of PCs, smartphones, high-speed internet, and more. This miniaturization trend has led to silicon chips today that have almost unimaginably small circuitry. Transistors, the tiny switches that implement computer microprocessors, are so small that 1,000 of them laid end-to-end are no wider than a human hair. And for a long time, the smaller the transistors were, the faster they could switch. But today, we’re approaching the limit of how small transistors can get. As a result, over the past decade researchers have been scratching their heads to find other ways to improve performance so that the computer industry can continue to innovate. While we wait for the maturation of new computing technologies like quantum, carbon nanotubes, or photonics (which may take a while), other approaches will be needed to get performance as Moore’s Law comes to an end. In a recent journal article published in Science, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) identifies three key areas to prioritize to continue to deliver computing speed-ups: better software, new algorithms, and more streamlined hardware. Senior author Charles E. Leiserson says that the performance benefits from miniaturization have been so great that, for decades, programmers have been able to prioritize making code-writing easier rather than making the code itself run faster. The inefficiency that this tendency introduces has been acceptable, because faster computer chips have always been able to pick up the slack. “But nowadays, being able to make further advances in fields like machine learning, robotics, and virtual reality will require huge amounts of computational power that miniaturization can no longer provide,” says Leiserson, the Edwin Sibley Webster Professor in MIT’s Department of Electrical Engineering and Computer Science. “If we want to harness the full potential of these technologies, we must change our approach to computing.” Leiserson co-wrote the paper, published this week, with Research Scientist Neil Thompson, professors Daniel Sanchez and Joel Emer, Adjunct Professor Butler Lampson, and research scientists Bradley Kuszmaul and Tao Schardl. No more Moore The authors make recommendations about three areas of computing: software, algorithms, and hardware architecture. With software, they say that programmers’ previous prioritization of productivity over performance has led to problematic strategies like “reduction”: taking code that worked on problem A and using it to solve problem B. For example, if someone has to create a system to recognize yes-or-no voice commands, but doesn’t want to code a whole new custom program, they could take an existing program that recognizes a wide range of words and tweak it to respond only to yes-or-no answers. While this approach reduces coding time, the inefficiencies it creates quickly compound: if a single reduction is 80 percent as efficient as a custom solution, and you then add 20 layers of reduction, the code will ultimately be 100 times less efficient than it could be. “These are the kinds of strategies that programmers have to rethink as hardware improvements slow down,” says Thompson. “We can’t keep doing ‘business as usual’ if we want to continue to get the speed-ups we’ve grown accustomed to.” Instead, the researchers recommend techniques like parallelizing code. Much existing software has been designed using ancient assumptions that processors can only do only one operation at a time. But in recent years multicore technology has enabled complex tasks to be completed thousands of times faster and in a much more energy-efficient way.  “Since Moore’s Law will not be handing us improved performance on a silver platter, we will have to deliver performance the hard way,” says Moshe Vardi, a professor in computational engineering at Rice University. “This is a great opportunity for computing research, and the [MIT CSAIL] report provides a road map for such research.”  As for algorithms, the team suggests a three-pronged approach that includes exploring new problem areas, addressing concerns about how algorithms scale, and tailoring them to better take advantage of modern hardware. Lastly, in terms of hardware architecture, the team advocates that hardware be streamlined so that problems can be solved with fewer transistors and less silicon. Streamlining includes using simpler processors and creating hardware tailored to specific applications, like the graphics-processing unit is tailored for computer graphics.  “Hardware customized for particular domains can be much more efficient and use far fewer transistors, enabling applications to run tens to hundreds of times faster,” says Schardl. “More generally, hardware streamlining would further encourage parallel programming, creating additional chip area to be used for more circuitry that can operate in parallel.” While these approaches may be the best path forward, the researchers say that it won’t always be an easy one. Organizations that use such techniques may not know the benefits of their efforts until after they’ve invested a lot of engineering time. Plus, the speed-ups won’t be as consistent as they were with Moore’s Law: they may be dramatic at first, and then require large amounts of effort for smaller improvements.  Certain companies have already gotten the memo. “For tech giants like Google and Amazon, the huge scale of their data centers means that even small improvements in software performance can result in large financial returns,” says Thompson.  “But while these firms may be leading the charge, many others will need to take these issues seriously if they want to stay competitive.” Getting improvements in the areas identified by the team will also require building up the infrastructure and workforce that make them possible.   “Performance growth will require new tools, programming languages, and hardware to facilitate more and better performance engineering,” says Leiserson. “It also means computer scientists being better educated about how we can make software, algorithms, and hardware work together, instead of putting them in different silos.” This work was supported, in part, by the National Science Foundation. We’re approaching the limit of how small transistors can get. As a result, over the past decade researchers have been working to find other ways to improve performance so that the computer industry can continue to innovate. https://news.mit.edu/2020/algorithm-simulates-roll-loaded-dice-0528 Approach for generating numbers at random may help analyses of complex systems, from Earth’s climate to financial markets. Thu, 28 May 2020 10:02:13 -0400 https://news.mit.edu/2020/algorithm-simulates-roll-loaded-dice-0528 Steve Nadis | MIT News correspondent The fast and efficient generation of random numbers has long been an important challenge. For centuries, games of chance have relied on the roll of a die, the flip of a coin, or the shuffling of cards to bring some randomness into the proceedings. In the second half of the 20th century, computers started taking over that role, for applications in cryptography, statistics, and artificial intelligence, as well as for various simulations — climatic, epidemiological, financial, and so forth. MIT researchers have now developed a computer algorithm that might, at least for some tasks, churn out random numbers with the best combination of speed, accuracy, and low memory requirements available today. The algorithm, called the Fast Loaded Dice Roller (FLDR), was created by MIT graduate student Feras Saad, Research Scientist Cameron Freer, Professor Martin Rinard, and Principal Research Scientist Vikash Mansinghka, and it will be presented next week at the 23rd International Conference on Artificial Intelligence and Statistics.  Simply put, FLDR is a computer program that simulates the roll of dice to produce random integers. The dice can have any number of sides, and they are “loaded,” or weighted, to make some sides more likely to come up than others. A loaded die can still yield random numbers — as one cannot predict in advance which side will turn up — but the randomness is constrained to meet a preset probability distribution. One might, for instance, use loaded dice to simulate the outcome of a baseball game; while the superior team is more likely to win, on a given day either team could end up on top. With FLDR, the dice are “perfectly” loaded, which means they exactly achieve the specified probabilities. With a four-sided die, for example, one could arrange things so that the numbers 1,2,3, and 4 turn up exactly 23 percent, 34 percent, 17 percent, and 26 percent of the time, respectively. To simulate the roll of loaded dice that have a large number of sides, the MIT team first had to draw on a simpler source of randomness — that being a computerized (binary) version of a coin toss, yielding either a 0 or a 1, each with 50 percent probability. The efficiency of their method, a key design criterion, depends on the number of times they have to tap into this random source — the number of “coin tosses,” in other words — to simulate each dice roll.  In a landmark 1976 paper, the computer scientists Donald Knuth and Andrew Yao devised an algorithm that could simulate the roll of loaded dice with the maximum efficiency theoretically attainable. “While their algorithm was optimally efficient with respect to time,” Saad explains, meaning that literally nothing could be faster, “it is inefficient in terms of the space, or computer memory, needed to store that information.” In fact, the amount of memory required grows exponentially, depending on the number of sides on the dice and other factors. That renders the Knuth-Yao method impractical, he says, except for special cases, despite its theoretical importance. FLDR was designed for greater utility. “We are almost as time efficient,” Saad says, “but orders of magnitude better in terms of memory efficiency.” FLDR can use up to 10,000 times less memory storage space than the Knuth-Yao approach, while taking no more than 1.5 times longer per operation. For now, FLDR’s main competitor is the Alias method, which has been the field’s dominant technology for decades. When analyzed theoretically, according to Freer, FLDR has one clear-cut advantage over Alias: It makes more efficient use of the random source — the “coin tosses,” to continue with that metaphor — than Alias. In certain cases, moreover, FLDR is also faster than Alias in generating rolls of loaded dice. FLDR, of course, is still brand new and has not yet seen widespread use. But its developers are already thinking of ways to improve its effectiveness through both software and hardware engineering. They also have specific applications in mind, apart from the general, ever-present need for random numbers. Where FLDR can help most, Mansinghka suggests, is by making so-called Monte Carlo simulations and Monte Carlo inference techniques more efficient. Just as FLDR uses coin flips to simulate the more complicated roll of weighted, many-sided dice, Monte Carlo simulations use a dice roll to generate more complex patterns of random numbers.  The United Nations, for instance, runs simulations of seismic activity that show when and where earthquakes, tremors, or nuclear tests are happening on the globe. The United Nations also carries out Monte Carlo inference: running random simulations that generate possible explanations for actual seismic data. This works by conducting a second series of Monte Carlo simulations, which randomly test out alternative parameters for an underlying seismic simulation to find the parameter values most likely to reproduce the observed data. These parameters contain information about when and where earthquakes and nuclear tests might actually have occurred.  “Monte Carlo inference can require hundreds of thousands of times more random numbers than Monte Carlo simulations,” Mansinghka says. “That’s one big bottleneck where FLDR could really help. Monte Carlo simulation and inference algorithms are also central to probabilistic programming, an emerging area of AI with broad applications.”  Ryan Rifkin, Director of Research at Google, sees great potential for FLDR in this regard. “Monte Carlo inference algorithms are central to modern AI engineering … and to large-scale statistical modeling,” says Rifkin, who was not involved in the study. “FLDR is an extremely promising development that may lead to ways to speed up the fundamental building blocks of random number generation, and might help Google make Monte Carlo inference significantly faster and more energy efficient.” Despite its seemingly bright future, FLDR almost did not come to light. Hints of it first emerged from a previous paper the same four MIT researchers published at a symposium in January, which introduced a separate algorithm. In that work, the authors showed that if a predetermined amount of memory were allocated for a computer program to simulate the roll of loaded dice, their algorithm could determine the minimum amount of “error” possible — that is, how close one comes toward meeting the designated probabilities for each side of the dice.  If one doesn’t limit the memory in advance, the error can be reduced to zero, but Saad noticed a variant with zero error that used substantially less memory and was nearly as fast. At first he thought the result might be too trivial to bother with. But he mentioned it to Freer who assured Saad that this avenue was worth pursuing. FLDR, which is error-free in this same respect, arose from those humble origins and now has a chance of becoming a leading technology in the realm of random number generation. That’s no trivial matter given that we live in a world that’s governed, to a large extent, by random processes — a principle that applies to the distribution of galaxies in the universe, as well as to the outcome of a spirited game of craps. A new algorithm, called the Fast Loaded Dice Roller (FLDR), simulates the roll of dice to produce random integers. The dice, in this case, could have any number of sides, and they are “loaded,” or weighted, to make some sides more likely to come up than others. Image: Jose-Luis Olivares, MIT https://news.mit.edu/2020/data-driven-response-pandemic-0515 Isolat, a volunteer collaboration organized by the Institute for Data, Systems, and Society, informs coronavirus policy by analyzing data associated with the pandemic. Fri, 15 May 2020 11:45:01 -0400 https://news.mit.edu/2020/data-driven-response-pandemic-0515 Scott Murray | Institute for Data, Systems, and Society The Covid-19 pandemic continues to challenge how societies and institutions function at macro and micro scales. In the United States, the novel coronavirus has affected everything from the economy to elections — and has raised difficult questions about MIT’s capacity to reopen in the fall. To help policymakers at MIT and beyond make informed decisions, the Institute for Data, Systems, and Society (IDSS) has formed a volunteer research group, Isolat, that provides analysis of pandemic-related data. “This pandemic has energized the broader IDSS community to bring crucial skills to bear,” says IDSS Director Munther Dahleh, a professor of electrical engineering and computer science (EECS). “Probability and statistics are tools for measuring uncertainty, and we have expertise within IDSS in using scientific information to impact policymaking.” The IDSS COVID-19 collaboration (Isolat) consists of MIT faculty, students, and researchers from different departments, as well as partners from around the world. Isolat members are statisticians, epidemiologists, data modelers, and policy researchers.  “There is strong IDSS representation in Isolat, from the Technology and Policy Program (TPP) and Statistics and Data Science Center (SDSC) to the Laboratory for Information and Decision Systems (LIDS),” says Dahleh. “This effort is driven by our community’s sense of social responsibility, both within IDSS and across MIT. And it’s given us a way to connect and build community in a time when we are far apart.” Real time, noisy data While there are a lot of data available related to Covid-19, there are also many questions about how complete or useful that data really are. The Isolat group is careful to identify the limits of what existing Covid-19 data can do. “Data is always useful, even if it’s noisy,” argues Dahleh. All the same, without widespread, randomized testing, it’s difficult for anyone to know the full extent of coronavirus spread. “We need to ask better questions that the data can answer,” adds Anette “Peko” Hosoi, an IDSS affiliate who is both a professor of mechanical engineering and associate dean of engineering. The Isolat group formed teams around three primary needs, each determined in consultation with stakeholders at MIT and the broader community. The Prediction team uses data on time-dependent variables to forecast infection growth rates and when the incidence of new cases should peak. The Intervention team strives to understand and quantify the outcomes of various policies and model “what-if” scenarios in order to make effective recommendations. The Data Infrastructure team gathers, organizes, and shares relevant data — early on they built a “data lake” to consolidate important datasets that are kept updated with Python scripts. Isolat meets every weekday via teleconference to discuss and vet projects and findings, which are published twice a week to the Isolat webpage. This kind of cross-disciplinary collaboration is typical of IDSS research, but the real-time dissemination of findings is a departure from academic methodology. “This is a different way of tackling the problem,” says Hosoi. “Everybody throws their contribution into the ring. We need answers today.” All the same, the group is mindful that the need for urgency does not eliminate the need for accuracy. “Quantification of the uncertainty in our results is key to providing actionable outcomes,” adds Hosoi. “We look forward to engaging the larger scientific community to make these findings more precise.” IDSS has also mobilized policy expertise to support Isolat researchers as they work to make their findings useful to MIT leaders and local governments. “We can help researchers think more critically about the ways in which their research is relevant to decision-making, when and with whom to engage, and what questions to ask” says Noelle Selin, a professor with IDSS and the Department of Earth, Atmospheric, and Planetary Sciences who is director of TPP.  Under their Research to Policy Engagement Initiative, TPP has begun hosting discussions with IDSS and LIDS faculty who are engaged with local communities to help them refine the kinds of questions they can answer for policymakers. Policy evaluation and what-if scenarios The available data on Covid-19 infection rates and deaths can indicate how fast those rates are changing, and it can indicate which interventions are more or less effective. This means Isolat researchers can not only measure the effectiveness of current policy, but forecast the potential impact of new policies or policy changes. To that end, Isolat researchers have designed and applied a method to predict policy impact that they call “Synthetic Interventions.” Leading this project is Devavrat Shah, an EECS professor and member of LIDS who directs the SDSC within IDSS. “Having a clear understanding of the trade-offs between interventions is crucial in charting a path forward on how to open up various sectors of society,” says Shah. “A key challenge is that policymakers do not have the luxury of actually enacting a variety of interventions and seeing which has the optimal outcome.” Based on a statistical method called synthetic control, the Synthetic Interventions method is a data-driven way to perform what-if scenario planning. The method leverages information from interventions that have already been enacted across the world, and fits this information to a policymaker’s setting of interest. For example, to estimate the effect of mobility-restricting interventions on the United States, Shah and his team used daily death data from countries with more extreme mobility restrictions to create a “synthetic low-mobility U.S.” and project the “counterfactual trajectory” — what could have happened — if the U.S. had applied similar interventions. “The good news,” says Shah, “is that so far our models suggest that moderate, precise restrictions in mobility, in particular at retail and transit locations, could play a key role in flattening the curve.” Are curves flattening? Another use of Covid-19 data is to model the growth and spread of the disease and predict when curves will flatten — when cases of the coronavirus will slow their exponential growth.  At first, Prediction team researchers looked at disease spread in U.S. states. But the availability of case count data at the county-level in the United States allowed Isolat researchers to model growth more granularly by fitting an exponential of a quadratic function to the cumulative number of cases reported in each county. “This analysis gives us a sense of how the epidemic spread varies within a state,” says Hamsa Balakrishnan, an IDSS affiliate who is both a professor and associate department head of aeronautics and astronautics. “A state or the nation as a whole may not be homogeneous in how the epidemic spreads.” Northern and southern California, for example, present two different pictures of spread when looked at county-by-county, suggesting that state officials should not necessarily apply one-size-fits-all policy solutions across the state. Similar differences can be seen in Massachusetts as well; Suffolk, Middlesex, and Norfolk counties all show a longer time to plateau than other counties in the state. Adds Balakrishnan: “Considering the influence of factors such as population density, demographics, neighboring counties, geography, and mobility can provide insights into the spread of Covid-19.” Impacting policy With daily meetings, two new posts per week, evolving groups and subteams, and new members joining each week, Isolat is a dynamic and uniquely MIT approach to the coronavirus crisis. But the group remains oriented around its purpose: to inform policymakers with data-driven recommendations. As Isolat researchers apply different approaches to seek answers to questions at larger scales, the group is also exploring questions related to reopening the MIT campus, and sharing information with others at MIT including the Team 2020 planning group and the We Solve For Fall project. The Isolat group has applied control theory to the problem, looking at the campus as a dynamic network. “Ultimately, the ingredients of control will be testing, distancing, and quarantining,” says Dahleh. “Testing is huge. If we don’t have a cure or a vaccine, testing and quarantining is the only way we can control the spread of infection.” Isolat researchers are informing MIT leaders, as well as building connections with local and state governments, advising groups abroad, and coordinating with engineers who are designing apps and solutions to pandemic challenges. They will continue to share their findings on the Isolat webpage. Researchers with the IDSS Covid-19 Collaboration (Isolat) are designing a control model for testing and isolating members of communities like MIT’s to reduce Covid-19 infection. https://news.mit.edu/2020/data-driven-response-pandemic-0515 Isolat, a volunteer collaboration organized by the Institute for Data, Systems, and Society, informs coronavirus policy by analyzing data associated with the pandemic. Fri, 15 May 2020 11:45:01 -0400 https://news.mit.edu/2020/data-driven-response-pandemic-0515 Scott Murray | Institute for Data, Systems, and Society The Covid-19 pandemic continues to challenge how societies and institutions function at macro and micro scales. In the United States, the novel coronavirus has affected everything from the economy to elections — and has raised difficult questions about MIT’s capacity to reopen in the fall. To help policymakers at MIT and beyond make informed decisions, the Institute for Data, Systems, and Society (IDSS) has formed a volunteer research group, Isolat, that provides analysis of pandemic-related data. “This pandemic has energized the broader IDSS community to bring crucial skills to bear,” says IDSS Director Munther Dahleh, a professor of electrical engineering and computer science (EECS). “Probability and statistics are tools for measuring uncertainty, and we have expertise within IDSS in using scientific information to impact policymaking.” The IDSS COVID-19 collaboration (Isolat) consists of MIT faculty, students, and researchers from different departments, as well as partners from around the world. Isolat members are statisticians, epidemiologists, data modelers, and policy researchers.  “There is strong IDSS representation in Isolat, from the Technology and Policy Program (TPP) and Statistics and Data Science Center (SDSC) to the Laboratory for Information and Decision Systems (LIDS),” says Dahleh. “This effort is driven by our community’s sense of social responsibility, both within IDSS and across MIT. And it’s given us a way to connect and build community in a time when we are far apart.” Real time, noisy data While there are a lot of data available related to Covid-19, there are also many questions about how complete or useful that data really are. The Isolat group is careful to identify the limits of what existing Covid-19 data can do. “Data is always useful, even if it’s noisy,” argues Dahleh. All the same, without widespread, randomized testing, it’s difficult for anyone to know the full extent of coronavirus spread. “We need to ask better questions that the data can answer,” adds Anette “Peko” Hosoi, an IDSS affiliate who is both a professor of mechanical engineering and associate dean of engineering. The Isolat group formed teams around three primary needs, each determined in consultation with stakeholders at MIT and the broader community. The Prediction team uses data on time-dependent variables to forecast infection growth rates and when the incidence of new cases should peak. The Intervention team strives to understand and quantify the outcomes of various policies and model “what-if” scenarios in order to make effective recommendations. The Data Infrastructure team gathers, organizes, and shares relevant data — early on they built a “data lake” to consolidate important datasets that are kept updated with Python scripts. Isolat meets every weekday via teleconference to discuss and vet projects and findings, which are published twice a week to the Isolat webpage. This kind of cross-disciplinary collaboration is typical of IDSS research, but the real-time dissemination of findings is a departure from academic methodology. “This is a different way of tackling the problem,” says Hosoi. “Everybody throws their contribution into the ring. We need answers today.” All the same, the group is mindful that the need for urgency does not eliminate the need for accuracy. “Quantification of the uncertainty in our results is key to providing actionable outcomes,” adds Hosoi. “We look forward to engaging the larger scientific community to make these findings more precise.” IDSS has also mobilized policy expertise to support Isolat researchers as they work to make their findings useful to MIT leaders and local governments. “We can help researchers think more critically about the ways in which their research is relevant to decision-making, when and with whom to engage, and what questions to ask” says Noelle Selin, a professor with IDSS and the Department of Earth, Atmospheric, and Planetary Sciences who is director of TPP.  Under their Research to Policy Engagement Initiative, TPP has begun hosting discussions with IDSS and LIDS faculty who are engaged with local communities to help them refine the kinds of questions they can answer for policymakers. Policy evaluation and what-if scenarios The available data on Covid-19 infection rates and deaths can indicate how fast those rates are changing, and it can indicate which interventions are more or less effective. This means Isolat researchers can not only measure the effectiveness of current policy, but forecast the potential impact of new policies or policy changes. To that end, Isolat researchers have designed and applied a method to predict policy impact that they call “Synthetic Interventions.” Leading this project is Devavrat Shah, an EECS professor and member of LIDS who directs the SDSC within IDSS. “Having a clear understanding of the trade-offs between interventions is crucial in charting a path forward on how to open up various sectors of society,” says Shah. “A key challenge is that policymakers do not have the luxury of actually enacting a variety of interventions and seeing which has the optimal outcome.” Based on a statistical method called synthetic control, the Synthetic Interventions method is a data-driven way to perform what-if scenario planning. The method leverages information from interventions that have already been enacted across the world, and fits this information to a policymaker’s setting of interest. For example, to estimate the effect of mobility-restricting interventions on the United States, Shah and his team used daily death data from countries with more extreme mobility restrictions to create a “synthetic low-mobility U.S.” and project the “counterfactual trajectory” — what could have happened — if the U.S. had applied similar interventions. “The good news,” says Shah, “is that so far our models suggest that moderate, precise restrictions in mobility, in particular at retail and transit locations, could play a key role in flattening the curve.” Are curves flattening? Another use of Covid-19 data is to model the growth and spread of the disease and predict when curves will flatten — when cases of the coronavirus will slow their exponential growth.  At first, Prediction team researchers looked at disease spread in U.S. states. But the availability of case count data at the county-level in the United States allowed Isolat researchers to model growth more granularly by fitting an exponential of a quadratic function to the cumulative number of cases reported in each county. “This analysis gives us a sense of how the epidemic spread varies within a state,” says Hamsa Balakrishnan, an IDSS affiliate who is both a professor and associate department head of aeronautics and astronautics. “A state or the nation as a whole may not be homogeneous in how the epidemic spreads.” Northern and southern California, for example, present two different pictures of spread when looked at county-by-county, suggesting that state officials should not necessarily apply one-size-fits-all policy solutions across the state. Similar differences can be seen in Massachusetts as well; Suffolk, Middlesex, and Norfolk counties all show a longer time to plateau than other counties in the state. Adds Balakrishnan: “Considering the influence of factors such as population density, demographics, neighboring counties, geography, and mobility can provide insights into the spread of Covid-19.” Impacting policy With daily meetings, two new posts per week, evolving groups and subteams, and new members joining each week, Isolat is a dynamic and uniquely MIT approach to the coronavirus crisis. But the group remains oriented around its purpose: to inform policymakers with data-driven recommendations. As Isolat researchers apply different approaches to seek answers to questions at larger scales, the group is also exploring questions related to reopening the MIT campus, and sharing information with others at MIT including the Team 2020 planning group and the We Solve For Fall project. The Isolat group has applied control theory to the problem, looking at the campus as a dynamic network. “Ultimately, the ingredients of control will be testing, distancing, and quarantining,” says Dahleh. “Testing is huge. If we don’t have a cure or a vaccine, testing and quarantining is the only way we can control the spread of infection.” Isolat researchers are informing MIT leaders, as well as building connections with local and state governments, advising groups abroad, and coordinating with engineers who are designing apps and solutions to pandemic challenges. They will continue to share their findings on the Isolat webpage. Researchers with the IDSS Covid-19 Collaboration (Isolat) are designing a control model for testing and isolating members of communities like MIT’s to reduce Covid-19 infection. https://news.mit.edu/2020/data-driven-response-pandemic-0515 Isolat, a volunteer collaboration organized by the Institute for Data, Systems, and Society, informs coronavirus policy by analyzing data associated with the pandemic. Fri, 15 May 2020 11:45:01 -0400 https://news.mit.edu/2020/data-driven-response-pandemic-0515 Scott Murray | Institute for Data, Systems, and Society The Covid-19 pandemic continues to challenge how societies and institutions function at macro and micro scales. In the United States, the novel coronavirus has affected everything from the economy to elections — and has raised difficult questions about MIT’s capacity to reopen in the fall. To help policymakers at MIT and beyond make informed decisions, the Institute for Data, Systems, and Society (IDSS) has formed a volunteer research group, Isolat, that provides analysis of pandemic-related data. “This pandemic has energized the broader IDSS community to bring crucial skills to bear,” says IDSS Director Munther Dahleh, a professor of electrical engineering and computer science (EECS). “Probability and statistics are tools for measuring uncertainty, and we have expertise within IDSS in using scientific information to impact policymaking.” The IDSS COVID-19 collaboration (Isolat) consists of MIT faculty, students, and researchers from different departments, as well as partners from around the world. Isolat members are statisticians, epidemiologists, data modelers, and policy researchers.  “There is strong IDSS representation in Isolat, from the Technology and Policy Program (TPP) and Statistics and Data Science Center (SDSC) to the Laboratory for Information and Decision Systems (LIDS),” says Dahleh. “This effort is driven by our community’s sense of social responsibility, both within IDSS and across MIT. And it’s given us a way to connect and build community in a time when we are far apart.” Real time, noisy data While there are a lot of data available related to Covid-19, there are also many questions about how complete or useful that data really are. The Isolat group is careful to identify the limits of what existing Covid-19 data can do. “Data is always useful, even if it’s noisy,” argues Dahleh. All the same, without widespread, randomized testing, it’s difficult for anyone to know the full extent of coronavirus spread. “We need to ask better questions that the data can answer,” adds Anette “Peko” Hosoi, an IDSS affiliate who is both a professor of mechanical engineering and associate dean of engineering. The Isolat group formed teams around three primary needs, each determined in consultation with stakeholders at MIT and the broader community. The Prediction team uses data on time-dependent variables to forecast infection growth rates and when the incidence of new cases should peak. The Intervention team strives to understand and quantify the outcomes of various policies and model “what-if” scenarios in order to make effective recommendations. The Data Infrastructure team gathers, organizes, and shares relevant data — early on they built a “data lake” to consolidate important datasets that are kept updated with Python scripts. Isolat meets every weekday via teleconference to discuss and vet projects and findings, which are published twice a week to the Isolat webpage. This kind of cross-disciplinary collaboration is typical of IDSS research, but the real-time dissemination of findings is a departure from academic methodology. “This is a different way of tackling the problem,” says Hosoi. “Everybody throws their contribution into the ring. We need answers today.” All the same, the group is mindful that the need for urgency does not eliminate the need for accuracy. “Quantification of the uncertainty in our results is key to providing actionable outcomes,” adds Hosoi. “We look forward to engaging the larger scientific community to make these findings more precise.” IDSS has also mobilized policy expertise to support Isolat researchers as they work to make their findings useful to MIT leaders and local governments. “We can help researchers think more critically about the ways in which their research is relevant to decision-making, when and with whom to engage, and what questions to ask” says Noelle Selin, a professor with IDSS and the Department of Earth, Atmospheric, and Planetary Sciences who is director of TPP.  Under their Research to Policy Engagement Initiative, TPP has begun hosting discussions with IDSS and LIDS faculty who are engaged with local communities to help them refine the kinds of questions they can answer for policymakers. Policy evaluation and what-if scenarios The available data on Covid-19 infection rates and deaths can indicate how fast those rates are changing, and it can indicate which interventions are more or less effective. This means Isolat researchers can not only measure the effectiveness of current policy, but forecast the potential impact of new policies or policy changes. To that end, Isolat researchers have designed and applied a method to predict policy impact that they call “Synthetic Interventions.” Leading this project is Devavrat Shah, an EECS professor and member of LIDS who directs the SDSC within IDSS. “Having a clear understanding of the trade-offs between interventions is crucial in charting a path forward on how to open up various sectors of society,” says Shah. “A key challenge is that policymakers do not have the luxury of actually enacting a variety of interventions and seeing which has the optimal outcome.” Based on a statistical method called synthetic control, the Synthetic Interventions method is a data-driven way to perform what-if scenario planning. The method leverages information from interventions that have already been enacted across the world, and fits this information to a policymaker’s setting of interest. For example, to estimate the effect of mobility-restricting interventions on the United States, Shah and his team used daily death data from countries with more extreme mobility restrictions to create a “synthetic low-mobility U.S.” and project the “counterfactual trajectory” — what could have happened — if the U.S. had applied similar interventions. “The good news,” says Shah, “is that so far our models suggest that moderate, precise restrictions in mobility, in particular at retail and transit locations, could play a key role in flattening the curve.” Are curves flattening? Another use of Covid-19 data is to model the growth and spread of the disease and predict when curves will flatten — when cases of the coronavirus will slow their exponential growth.  At first, Prediction team researchers looked at disease spread in U.S. states. But the availability of case count data at the county-level in the United States allowed Isolat researchers to model growth more granularly by fitting an exponential of a quadratic function to the cumulative number of cases reported in each county. “This analysis gives us a sense of how the epidemic spread varies within a state,” says Hamsa Balakrishnan, an IDSS affiliate who is both a professor and associate department head of aeronautics and astronautics. “A state or the nation as a whole may not be homogeneous in how the epidemic spreads.” Northern and southern California, for example, present two different pictures of spread when looked at county-by-county, suggesting that state officials should not necessarily apply one-size-fits-all policy solutions across the state. Similar differences can be seen in Massachusetts as well; Suffolk, Middlesex, and Norfolk counties all show a longer time to plateau than other counties in the state. Adds Balakrishnan: “Considering the influence of factors such as population density, demographics, neighboring counties, geography, and mobility can provide insights into the spread of Covid-19.” Impacting policy With daily meetings, two new posts per week, evolving groups and subteams, and new members joining each week, Isolat is a dynamic and uniquely MIT approach to the coronavirus crisis. But the group remains oriented around its purpose: to inform policymakers with data-driven recommendations. As Isolat researchers apply different approaches to seek answers to questions at larger scales, the group is also exploring questions related to reopening the MIT campus, and sharing information with others at MIT including the Team 2020 planning group and the We Solve For Fall project. The Isolat group has applied control theory to the problem, looking at the campus as a dynamic network. “Ultimately, the ingredients of control will be testing, distancing, and quarantining,” says Dahleh. “Testing is huge. If we don’t have a cure or a vaccine, testing and quarantining is the only way we can control the spread of infection.” Isolat researchers are informing MIT leaders, as well as building connections with local and state governments, advising groups abroad, and coordinating with engineers who are designing apps and solutions to pandemic challenges. They will continue to share their findings on the Isolat webpage. Researchers with the IDSS Covid-19 Collaboration (Isolat) are designing a control model for testing and isolating members of communities like MIT’s to reduce Covid-19 infection. https://news.mit.edu/2020/data-driven-response-pandemic-0515 Isolat, a volunteer collaboration organized by the Institute for Data, Systems, and Society, informs coronavirus policy by analyzing data associated with the pandemic. Fri, 15 May 2020 11:45:01 -0400 https://news.mit.edu/2020/data-driven-response-pandemic-0515 Scott Murray | Institute for Data, Systems, and Society The Covid-19 pandemic continues to challenge how societies and institutions function at macro and micro scales. In the United States, the novel coronavirus has affected everything from the economy to elections — and has raised difficult questions about MIT’s capacity to reopen in the fall. To help policymakers at MIT and beyond make informed decisions, the Institute for Data, Systems, and Society (IDSS) has formed a volunteer research group, Isolat, that provides analysis of pandemic-related data. “This pandemic has energized the broader IDSS community to bring crucial skills to bear,” says IDSS Director Munther Dahleh, a professor of electrical engineering and computer science (EECS). “Probability and statistics are tools for measuring uncertainty, and we have expertise within IDSS in using scientific information to impact policymaking.” The IDSS COVID-19 collaboration (Isolat) consists of MIT faculty, students, and researchers from different departments, as well as partners from around the world. Isolat members are statisticians, epidemiologists, data modelers, and policy researchers.  “There is strong IDSS representation in Isolat, from the Technology and Policy Program (TPP) and Statistics and Data Science Center (SDSC) to the Laboratory for Information and Decision Systems (LIDS),” says Dahleh. “This effort is driven by our community’s sense of social responsibility, both within IDSS and across MIT. And it’s given us a way to connect and build community in a time when we are far apart.” Real time, noisy data While there are a lot of data available related to Covid-19, there are also many questions about how complete or useful that data really are. The Isolat group is careful to identify the limits of what existing Covid-19 data can do. “Data is always useful, even if it’s noisy,” argues Dahleh. All the same, without widespread, randomized testing, it’s difficult for anyone to know the full extent of coronavirus spread. “We need to ask better questions that the data can answer,” adds Anette “Peko” Hosoi, an IDSS affiliate who is both a professor of mechanical engineering and associate dean of engineering. The Isolat group formed teams around three primary needs, each determined in consultation with stakeholders at MIT and the broader community. The Prediction team uses data on time-dependent variables to forecast infection growth rates and when the incidence of new cases should peak. The Intervention team strives to understand and quantify the outcomes of various policies and model “what-if” scenarios in order to make effective recommendations. The Data Infrastructure team gathers, organizes, and shares relevant data — early on they built a “data lake” to consolidate important datasets that are kept updated with Python scripts. Isolat meets every weekday via teleconference to discuss and vet projects and findings, which are published twice a week to the Isolat webpage. This kind of cross-disciplinary collaboration is typical of IDSS research, but the real-time dissemination of findings is a departure from academic methodology. “This is a different way of tackling the problem,” says Hosoi. “Everybody throws their contribution into the ring. We need answers today.” All the same, the group is mindful that the need for urgency does not eliminate the need for accuracy. “Quantification of the uncertainty in our results is key to providing actionable outcomes,” adds Hosoi. “We look forward to engaging the larger scientific community to make these findings more precise.” IDSS has also mobilized policy expertise to support Isolat researchers as they work to make their findings useful to MIT leaders and local governments. “We can help researchers think more critically about the ways in which their research is relevant to decision-making, when and with whom to engage, and what questions to ask” says Noelle Selin, a professor with IDSS and the Department of Earth, Atmospheric, and Planetary Sciences who is director of TPP.  Under their Research to Policy Engagement Initiative, TPP has begun hosting discussions with IDSS and LIDS faculty who are engaged with local communities to help them refine the kinds of questions they can answer for policymakers. Policy evaluation and what-if scenarios The available data on Covid-19 infection rates and deaths can indicate how fast those rates are changing, and it can indicate which interventions are more or less effective. This means Isolat researchers can not only measure the effectiveness of current policy, but forecast the potential impact of new policies or policy changes. To that end, Isolat researchers have designed and applied a method to predict policy impact that they call “Synthetic Interventions.” Leading this project is Devavrat Shah, an EECS professor and member of LIDS who directs the SDSC within IDSS. “Having a clear understanding of the trade-offs between interventions is crucial in charting a path forward on how to open up various sectors of society,” says Shah. “A key challenge is that policymakers do not have the luxury of actually enacting a variety of interventions and seeing which has the optimal outcome.” Based on a statistical method called synthetic control, the Synthetic Interventions method is a data-driven way to perform what-if scenario planning. The method leverages information from interventions that have already been enacted across the world, and fits this information to a policymaker’s setting of interest. For example, to estimate the effect of mobility-restricting interventions on the United States, Shah and his team used daily death data from countries with more extreme mobility restrictions to create a “synthetic low-mobility U.S.” and project the “counterfactual trajectory” — what could have happened — if the U.S. had applied similar interventions. “The good news,” says Shah, “is that so far our models suggest that moderate, precise restrictions in mobility, in particular at retail and transit locations, could play a key role in flattening the curve.” Are curves flattening? Another use of Covid-19 data is to model the growth and spread of the disease and predict when curves will flatten — when cases of the coronavirus will slow their exponential growth.  At first, Prediction team researchers looked at disease spread in U.S. states. But the availability of case count data at the county-level in the United States allowed Isolat researchers to model growth more granularly by fitting an exponential of a quadratic function to the cumulative number of cases reported in each county. “This analysis gives us a sense of how the epidemic spread varies within a state,” says Hamsa Balakrishnan, an IDSS affiliate who is both a professor and associate department head of aeronautics and astronautics. “A state or the nation as a whole may not be homogeneous in how the epidemic spreads.” Northern and southern California, for example, present two different pictures of spread when looked at county-by-county, suggesting that state officials should not necessarily apply one-size-fits-all policy solutions across the state. Similar differences can be seen in Massachusetts as well; Suffolk, Middlesex, and Norfolk counties all show a longer time to plateau than other counties in the state. Adds Balakrishnan: “Considering the influence of factors such as population density, demographics, neighboring counties, geography, and mobility can provide insights into the spread of Covid-19.” Impacting policy With daily meetings, two new posts per week, evolving groups and subteams, and new members joining each week, Isolat is a dynamic and uniquely MIT approach to the coronavirus crisis. But the group remains oriented around its purpose: to inform policymakers with data-driven recommendations. As Isolat researchers apply different approaches to seek answers to questions at larger scales, the group is also exploring questions related to reopening the MIT campus, and sharing information with others at MIT including the Team 2020 planning group and the We Solve For Fall project. The Isolat group has applied control theory to the problem, looking at the campus as a dynamic network. “Ultimately, the ingredients of control will be testing, distancing, and quarantining,” says Dahleh. “Testing is huge. If we don’t have a cure or a vaccine, testing and quarantining is the only way we can control the spread of infection.” Isolat researchers are informing MIT leaders, as well as building connections with local and state governments, advising groups abroad, and coordinating with engineers who are designing apps and solutions to pandemic challenges. They will continue to share their findings on the Isolat webpage. Researchers with the IDSS Covid-19 Collaboration (Isolat) are designing a control model for testing and isolating members of communities like MIT’s to reduce Covid-19 infection. https://news.mit.edu/2020/reporting-tool-balance-hospitals-covid-19-0424 Based on crowdsourced data, app helps patients, EMTs, and physicians determine real-time availability of hospital resources. Thu, 23 Apr 2020 23:59:59 -0400 https://news.mit.edu/2020/reporting-tool-balance-hospitals-covid-19-0424 Jennifer Chu | MIT News Office As cases of Covid-19 continue to climb in parts of the United States, the number of people seeking treatment is threatening to overwhelm many hospitals, forcing some facilities to ration their care and reserve ventilators, hospital beds, and other limited medical resources for the sickest patients.  Having a handle on local hospitals’ capacity and resource availability could help balance the load of Covid-19 patients requiring hospitalization across a region, for instance allowing an EMT to send a patient to a facility where they are more likely to be treated quickly. But many states lack real-time data on their current capacity to treat Covid-19 patients.  A group of researchers in MIT’s Computer Science and Intelligence Laboratory (CSAIL), working with the MIT spinoff Mobi Systems, are aiming to help level demand across the entire health care network by providing real-time updates of hospital resources, which they hope will help patients, EMTs, and physicians quickly decide which facility is best equipped to handle a new patient at any given time.  The team has developed a web app which is now publicly accessible at: https://Covid19hospitalstatus.com. The interface allows users such as patients, nurses, and doctors to report a hospital’s current status in a number of metrics, from the average wait time (something that a patient may get a sense for as they spend time in a waiting room), to the number of ventilators and ICU beds, which doctors and nurses may be able to approximate. EMTS can use the app as a map, zooming in by state, county, or city to quickly gauge hospital capacity, and decide which nearby hospitals have available beds where they can send a patient requiring hospitalization. The app can also generate a list of hospitals, prioritized by availability, time of travel, and most recently updated data.  “We want to flatten the Covid curve by physical distancing over the course of months,” says MIT graduate Anna Jaffe ’07, CEO of Mobi Systems. “But there’s another curve to flatten, which is this real-time challenge of getting the right patient to the right hospital, in the right moment, to level the load on hospitals and health care workers.” “Do something” As the pandemic began to unfold around the world, Jaffe was intrigued by the results of a short hackathon that one Mobi member, Julius Pätzold, recently attended in Germany. The weekend challenge, sponsored by the German government, included a problem to match supply and demand, for instance in a hospital facing a surge in patient visits.  His team mapped the German hospital infrastructure, including the status of individual hospitals’ capacity, then simulated dispatching patients to hospitals according to a hospital’s capacity, its relative location to a patient, and a patient’s medical needs. The real-time maps developed over this short time suggested such tools would have a positive impact on a patient’s quality of care, specifically in decreasing death rates. “That intersected with my feeling that I think everyone wants to do something around Covid-19 in response to the current crisis, and not just be cooped up in our respective homes,” says Jaffe, whose company, Mobi Systems, develops tools for large-scale network optimization problems surrounding mobility and hospitality.  Mobi originally grew out of CSAIL’s Model-based Embedded Robotic Systems group, led by MIT Professor Brian Williams, whose work involves developing autonomous planning tools to help individuals make complex, real-time decisions in the face of uncertainty and risk.  Jaffe reached out to Williams to help develop a web-based reporting tool for hospitals, to similarly help patients and medical professionals make critical, real-time decisions of where best to send a patient, based on resource availability.  “Our question was, how can the resources statewide or nationwide be used most effectively, in order to keep the most people healthy,” Williams says. “And for the individual, which hospital will meet their needs, and how do they get there. That’s the exercise we’re tackling here.” Crowd power The team’s app is heavily dependent on crowdsourced data, and the willingness of patients and medical professionals to report on various metrics, from a hospital’s current wait time to the approximate number of ICU beds and ventilators available.  “The reporting options right now are very specific,” Jaffe says. “But what we really want to know is, can your hospital accept a patient right now?”  A user can enter their role — patient, nurse, or physician — then report on, for instance, a hospital’s average wait time. With a sliding scale, they can rate their confidence in their report before submitting it.  But what if those users are reporting false or inaccurate data, whether intentionally or not?  Williams says in order to guard against such uncertainty, the team takes a probabilistic approach. For instance, the app assumes that one user’s reporting of a hospital’s status is one of low confidence, which is initially not weighed heavily in the overall estimation for that metric. They can then incorporate this one data point into all the other reports they’ve received for that metric. If most of those reports have also been rated with low confidence, but report the same result, that estimate, such as of wait time, is automatically weighed more heavily, and therefore rated at a higher confidence overall.   Additionally, he says if the app receives reports from more trusted sources — for instance, if hospitals make in-house, aggregated data available to the app — those sources would “swamp out” or take higher priority over low-confidence reports of the same metric.  The team is testing the app with just such a trustworthy dataset, from the state of Pennsylvania, which for the last several years has had a system in place for hospitals to report resource availability, that is updated at least twice a day. The team has used data from the last week to track Covid-19 visits across the state’s hospital system. “In this data, you can see that not all hospitals are overrun — there are clear differences in availability,” says MIT graduate Peng Yu ’SM 13, ’PhD 17, chief technology officer at Mobi, highlighting the potential for distributing patients across a region’s hospitals, to balance resources across a hospital network.  However, most states lack such aggregated, updated information. In most other states, for instance, EMTs either have a handful of default facilities where they typically send patients, or they have to call around to surrounding hospitals to check availability.  “It’s really about word of mouth — who do you know, and who do you call up,” says Williams, whose nephew is an EMT who has worked in regions with varying decision-making practices. “We’re trying to aggregate that information, to make these recommendations much faster. The team is now reaching out to thousands of medical professionals to test-drive the reporting tool, in hopes of boosting the crowdsourcing component for the app, which is now available on any internet-enabled device. To address the pandemic, the team believes that data need to be made available at a faster rate than the virus’ spread. Their hope is that states will follow in Pennsylvania’s footsteps and, for instance, mandate that hospitals report resource data, and provide reporting tools such as the new app to doctors and EMTs.  “This project is very much for the people, by the people, and will be kept open and free,” Williams says.   “Unfortunately, it doesn’t feel like this is a flash pandemic,” Jaffe says. “Even in a recovery period, hospitals will have to resume normal care, concurrently with treating Covid-19 over time. Our app may help load balance in that way as well, so hospitals can more effectively predict how many floors they need to quarantine for Covid-19, so that the rest of the hospital can go back to things like having families around a mother giving birth. We aim to really understand how to bring things back to a more normal operational status, while still handling the crisis. A new hospital status app aims to balance hospitals’ Covid-19 load. Based on crowdsourced data, the app gives patients, EMTs, and physicians tools to report and check on availability of hospital resources, from ventilators and ICU beds to average wait times. Image: MIT News. Image on tablet by Lorea Sinclaire https://news.mit.edu/2020/jean-pauphilet-operations-research-0423 “Operations in practice are very messy, but I think that’s what makes them exciting,” says graduate student Jean Pauphilet. Wed, 22 Apr 2020 23:59:59 -0400 https://news.mit.edu/2020/jean-pauphilet-operations-research-0423 Bridget E. Begg | Office of Graduate Education When he began his engineering program at École Polytechnique in his hometown of Paris, Jean Pauphilet did not aspire to the academy.“I used to associate academia with fundamental research, which I don’t enjoy much,” he says. “But slowly, I discovered another type of research, where people use rigorous scientific principles for applied and impactful projects.”A fascination with projects that have direct applications to organizational problems led Pauphilet to the field of operations research and analytics — and to a PhD at the Operations Research Center (ORC), a joint program between the MIT Stephen A. Schwarzman College of Computing and the MIT Sloan School of Management.Operations research models decision-making processes as mathematical optimization problems, such as planning for energy production given unpredictable fluctuations in demand. It’s a complex subject that Pauphilet finds exhilarating. “Operations in practice are very messy, but I think that’s what makes them exciting. You’re never short on problems to solve,” he says.Working in the lab of Professor Dimitris Bertsimas, and in collaboration with Beth Israel Deaconess Medical Center, Pauphilet focuses on solving challenges in the health care field. For example, how can hospitals best make bed assignments and staffing decisions? These types of logistical decisions are “a pain point for everyone,” he notes.“You really feel that you’re making peoples’ lives easier because when you’re talking about it to doctors and nurses, you realize that they don’t like to do it, they’re not trained at it, and it’s keeping them from actually doing their job. So, for me it was clear that it had a positive impact on their workload.” More recently, he has been involved in a group effort led by his advisor to develop analytics tools to inform policymakers and health care managers during the COVID-19 pandemic.Becoming an expertAs the son of two doctors, Pauphilet is already comfortable working within the medical field. He also feels well-prepared by his training in France, which allows students to choose their majors late and emphasizes a background in math. “Operations research requires versatility,” he explains. “Methodologically, it can involve anything ranging from probability theory to optimization algorithms and machine learning. So, having a strong and wide math background definitely helps.”This mentality has allowed him to grow into an expert in his field at MIT. “I’m less scared of research now,” he explains, “You might not find what you were expecting, but you always find something that is relevant to someone. So [research] is uncertain, but not risky. You can always get back on your feet in some way.” It’s a mentality that’s given him the confidence to find, solve, and address operations problems in novel ways in collaboration with companies and hospitals.Pauphilet, who will join London Business School as an assistant professor in the fall, has found himself thinking about the different pedagogical philosophies in the U.S. and France. At MIT, he completed the Kaufman Teaching Certificate Program to become more familiar with aspects of teaching not typically experienced as a teaching assistant, such as designing a course, writing lectures, and creating assignments.“Coming from France and teaching in the U.S., I think it’s especially interesting to learn from other peoples’ experience and to compare what their first experience of learning was at their universities in their countries. Also [it’s challenging] to define what is the best method of teaching that you can think of that acknowledges the differences between the students and the way they learn, and to try to take that into account in your own teaching style.”Culture and communityIn his free time, before the Covid-19 emergency, Pauphilet often took advantage of cultural and intellectual offerings in Cambridge and Boston. He frequented the Boston Symphony Orchestra (which offered $25 tickets for people under 40) and enjoys hearing unfamiliar composers and music, especially contemporary music with surprising new elements.Pauphilet is an avid chef who relishes the challenge of cooking large pieces of meat, such as whole turkeys or lamb shoulders, for friends. Beyond the food, he enjoys the long conversations that these meals facilitate and that people can’t necessarily experience in a restaurant. (As an aside he notes, “I think the service in a restaurant here is much more efficient than in Europe!”).Pauphilet has also been the president of MIT’s French Club, which organizes a variety of events for around 100 French-speaking graduate students, postdocs, and undergraduates. Though his undergraduate institution is well-represented at MIT, Pauphilet feels strongly about creating a network for those Francophones who may not have his luck, so they can feel as at home as he does.Now at the end of his PhD, Pauphilet has the chance to reflect on his experiences over the past three and a half years. In particular, he has found a deep sense of community in his cohort, lab, and community here. He attributes some of that to his graduate program’s structure — which begins with two required classes that everyone in the cohort takes together — but that’s just one aspect of the investment in building community Pauphilet has felt at MIT.“It’s a great environment. Honestly, I find that everyone is very mindful of students. I have a great relationship with my advisor that is not only based on research, and I think that’s very important,” he says.Overall, Pauphilet attributes his significant personal and professional growth in grad school to learning in MIT’s collaborative and open environment. And, he notes, being at the Institute has affected him in another important way.“I’m a bit nerdier than I used to be!” Graduate student Jean Pauphilet is a French PhD student in the Operations Research Center. Images: Gretchen Ertl https://news.mit.edu/2020/mit-idss-promoting-women-data-science-0417 WiDS Cambridge, co-hosted by the Institute for Data, Systems, and Society, recognizes and empowers women in STEM across a variety of disciplines. Fri, 17 Apr 2020 10:20:01 -0400 https://news.mit.edu/2020/mit-idss-promoting-women-data-science-0417 Institute for Data, Systems, and Society What do radiation waves from space, the U.S. Census, and the human genome have in common? All three, like so many things today, involve massive amounts of data. These data can unlock insights and lead to new solutions and better decision-making — for those who have the knowledge and tools to analyze it. The impressive variety of applications for data science tools and techniques were on display at the Women in Data Science Conference (WiDS Cambridge), held at the Microsoft NERD Center in early March, before MIT and the Commonwealth of Massachusetts began to de-densify in response to the Covid-19 emergency. Co-hosted by the Institute for Data, Systems, and Society (IDSS), the Harvard Institute for Applied Computational Science, and Microsoft Research New England, WiDS Cambridge is one of dozens of satellite WiDS events around the world. The program showcases women who are not only using data science tools in their research or business, but who are leaders refining those tools and recruiting more women into the field. The day’s signature event was a panel discussion on data science and fake news called “Data weaponized, data scrutinized: a war on information.” The panel was moderated by Manon Revel, a doctoral student in the IDSS Social and Engineering Systems (SES) program whose research has analyzed popup ads to see how exposure influences readers’ assessment of news credibility. Addressing current challenges, Manon shared: “Understanding the effect of false information and combatting it is crucial. It requires thinking through the technology design, but also the regulatory framework and the political and social context.” The panel also included Camille Francois, chief information officer for Graphika, a social network analysis startup that uses AI to understand online communities. “We don’t know how to measure the impact of foreign interference for many complicated reasons,” said Francois. “The aim of a foreign interference campaign is not necessarily to impact a vote. It’s to divide, it’s to confuse, and it’s to create chaos. How do you measure chaos?” In addition to the discussion on misinformation, WiDS Cambridge featured a wide variety of insights from industry and academia. Asu Ozdaglar, deputy dean of academics, head of the Department of Electrical Engineering and Computer Science, and faculty member in IDSS and the Laboratory for Information and Decision Systems (LIDS), highlighted robustness in machine learning. Citing the common example of image classification system errors, she explored how ‘perturbed’ data can, with small variations, disrupt otherwise accurate models, and offered a ‘minmax’ approach using generative adversarial networks (GANs) to increase robustness. For an industry perspective, Jess Stauth, managing director at Fidelity Labs, provided ways to apply basic research principles to modern data science business problems. Data science is a collection of tools from statistics to computing, she says, and businesses require infrastructure to use them to create tangible business value. “A data scientist alone in a room with a laptop is probably not going to be all that successful,” she muses. The conference provided opportunities for participants to network and job search, with sponsor companies hosting recruiting tables and answering questions. WiDS also empowered newer practitioners with a student and postdoc poster session and lightning talks. Over 30 poster presenters participated, showcasing work in fields as diverse as demographic bias in natural language processing, crime prediction, neurodegenerative disease, and sustainable buildings. “WiDS is a wonderful event where you can interact with your peers, present your research, and build confidence,” says Marie Charpignon, a graduate student in MIT’s SES PhD program who presented a poster on using causal inference on electronic health records to explore repurposing diabetes medication to treat dementia. “The conference brings together students, professors, industry researchers, and even venture capitalists in search of promising ideas. WiDS gives you a sense of the myriad paths you could take after graduation.” WiDS Cambridge, co-hosted by the Institute for Data, Systems, and Society, recognized and empowered women in STEM across a variety of disciplines. Photo: Dana J. Quigley Photography https://news.mit.edu/2020/mit-idss-promoting-women-data-science-0417 WiDS Cambridge, co-hosted by the Institute for Data, Systems, and Society, recognizes and empowers women in STEM across a variety of disciplines. Fri, 17 Apr 2020 10:20:01 -0400 https://news.mit.edu/2020/mit-idss-promoting-women-data-science-0417 Institute for Data, Systems, and Society What do radiation waves from space, the U.S. Census, and the human genome have in common? All three, like so many things today, involve massive amounts of data. These data can unlock insights and lead to new solutions and better decision-making — for those who have the knowledge and tools to analyze it. The impressive variety of applications for data science tools and techniques were on display at the Women in Data Science Conference (WiDS Cambridge), held at the Microsoft NERD Center in early March, before MIT and the Commonwealth of Massachusetts began to de-densify in response to the Covid-19 emergency. Co-hosted by the Institute for Data, Systems, and Society (IDSS), the Harvard Institute for Applied Computational Science, and Microsoft Research New England, WiDS Cambridge is one of dozens of satellite WiDS events around the world. The program showcases women who are not only using data science tools in their research or business, but who are leaders refining those tools and recruiting more women into the field. The day’s signature event was a panel discussion on data science and fake news called “Data weaponized, data scrutinized: a war on information.” The panel was moderated by Manon Revel, a doctoral student in the IDSS Social and Engineering Systems (SES) program whose research has analyzed popup ads to see how exposure influences readers’ assessment of news credibility. Addressing current challenges, Manon shared: “Understanding the effect of false information and combatting it is crucial. It requires thinking through the technology design, but also the regulatory framework and the political and social context.” The panel also included Camille Francois, chief information officer for Graphika, a social network analysis startup that uses AI to understand online communities. “We don’t know how to measure the impact of foreign interference for many complicated reasons,” said Francois. “The aim of a foreign interference campaign is not necessarily to impact a vote. It’s to divide, it’s to confuse, and it’s to create chaos. How do you measure chaos?” In addition to the discussion on misinformation, WiDS Cambridge featured a wide variety of insights from industry and academia. Asu Ozdaglar, deputy dean of academics, head of the Department of Electrical Engineering and Computer Science, and faculty member in IDSS and the Laboratory for Information and Decision Systems (LIDS), highlighted robustness in machine learning. Citing the common example of image classification system errors, she explored how ‘perturbed’ data can, with small variations, disrupt otherwise accurate models, and offered a ‘minmax’ approach using generative adversarial networks (GANs) to increase robustness. For an industry perspective, Jess Stauth, managing director at Fidelity Labs, provided ways to apply basic research principles to modern data science business problems. Data science is a collection of tools from statistics to computing, she says, and businesses require infrastructure to use them to create tangible business value. “A data scientist alone in a room with a laptop is probably not going to be all that successful,” she muses. The conference provided opportunities for participants to network and job search, with sponsor companies hosting recruiting tables and answering questions. WiDS also empowered newer practitioners with a student and postdoc poster session and lightning talks. Over 30 poster presenters participated, showcasing work in fields as diverse as demographic bias in natural language processing, crime prediction, neurodegenerative disease, and sustainable buildings. “WiDS is a wonderful event where you can interact with your peers, present your research, and build confidence,” says Marie Charpignon, a graduate student in MIT’s SES PhD program who presented a poster on using causal inference on electronic health records to explore repurposing diabetes medication to treat dementia. “The conference brings together students, professors, industry researchers, and even venture capitalists in search of promising ideas. WiDS gives you a sense of the myriad paths you could take after graduation.” WiDS Cambridge, co-hosted by the Institute for Data, Systems, and Society, recognized and empowered women in STEM across a variety of disciplines. Photo: Dana J. Quigley Photography https://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414 “Data scientists and visualization designers need to take their civic role very seriously in a pandemic,” says the MIT assistant professor. Mon, 13 Apr 2020 23:59:59 -0400 https://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414 Jennifer Chu | MIT News Office The Covid-19 pandemic is generating waves of data points from around the world, recording the number of tests performed, cases confirmed, patients recovered, and people who have died from the virus. As these data are continuously updated, media outlets, government agencies, academics, and data-packaging firms are racing to make sense of the numbers, using novel design and visualization tools to chart and graph the virus many different contexts.In general, data visualizations can help people quickly distill an otherwise overwhelming flood of numbers. Catherine D’Ignazio, assistant professor of urban science and planning at MIT, says it is critical that data are visualized responsibly in a pandemic.D’Ignazio is the director of the Data and Feminism Lab, where she uses data and computational techniques to work toward gender and racial equity. MIT News spoke with her about the current boom in Covid-19 data visualizations, and how data visualizers can help us make sense of the pandemic’s uncertain numbers. Q: How have you seen data visualization of Covid-19 evolve in the last few months, since the virus began its spread? A: The first thing I’ll note is that there has been an explosion of data visualization. Since the information about the virus comes in numbers — case counts, death counts, testing rates — it lends itself easily to data visualization. Maps, bar charts, and line charts of confirmed cases predominated at first, and I would say they are still the most common forms of visualization that we are seeing in media reporting and on social media. As a person in the field, the proliferation is both exciting, because it shows the relevance of visualization, and scary, because there is definitely some irresponsible use of visualization. Many high-profile organizations are plotting case counts on graduated color maps, which is a big no-no unless you have normalized your numbers. So California, a big and densely populated state, will always appear to be worse off in absolute raw case counts. Conversely, this way of plotting could cause you to miss small states with a high rate of infection since they will be low in relative case numbers and would always show up in lighter colors on the map. Second, as the crisis has developed, media outlets are mapping things other than simply case counts or death rates. There have been many versions of the “flatten the curve” chart. This one is interesting because it’s not about plotting specific numbers, but about explaining a public health concept to a broad audience with a hypothetical chart. The best visual explanation I’ve seen of the flatten the curve concept is from The Washington Post and comes with simulations and animations that explain virus transmission. There have also been a number of visualizations of how social distancing has changed people’s mobility behavior, shifting traffic patterns, and even a global satellite map where you can see how Covid-19 has reduced urban pollution over the past three months. Finally, this crisis is posing some difficult visual communication problems: How do you depict exponential growth in an accessible way? How do you visually explain the uncertainty in numbers like case counts, where we (at least in the U.S. context) have not done nearly enough testing to make them a reliable indicator of actual cases?  Journalists and health communicators have responded to these challenges by developing new visual conventions, as well as making heavy use of explanations and disclaimers in the narratives themselves. For example, the chart below, by Lisa Charlotte Rost for DataWrapper, uses a log scale on the y-axis for showing exponential rates of change. But note the dotted reference lines, labeled “deaths double every day” or “…every 2nd day.” These annotations help to highlight the use of the log scale (which otherwise might go unnoticed by readers) as well as to explain how to interpret the different slopes of the lines. Likewise, Rost is explicit about only making charts of death rates, not case counts, because of the variation in availability of tests and vast underreporting in many countries. Whereas actual cases may or may not be detected and counted, deaths are more likely to be counted.  A screenshot of an interactive chart, from Datawrapper, shows cumulative numbers of confirmed deaths due to the Covid-19 disease. Chart: Lisa Charlotte Rost, Datawrapper. Source: Johns Hopkins CSSE. Created with Datawrapper. Q: What are some things people should keep in mind when digging into available datasets to make their own visualizations? A: This is such a great question, because there has been a proliferation of visualizations and models that are not only erroneous but also irresponsible in a public health crisis. Usually these are made by folks who do not have expertise in epidemiology but assume that their skills in data science can just be magically ported into a new realm. I’d like to shout out here to Amanda Makulec’s excellent guidance on undertaking responsible data visualizations in a public health crisis. One of her main points is to consider simply not making another Covid-19 chart. What this points to is the idea that data scientists and visualization designers need to take their civic role very seriously in a pandemic. Following Makulec’s line of reasoning, designers can think of the visualization they are making in the context of decision support: Their visualization has the power to help people decide whether to reject public health guidance and go out, to stay home, to feel the gravity of the problem without being overwhelmed, or to panic and buy up all the toilet paper.  Data visualization carries the aura of objectivity and authority. If designers wield that authority irresponsibly — for example, by depicting case counts with clean, certain-looking lines when we know that there is deep uncertainty in how case counts in different places were collected — it may deplete public trust, lead to rejecting public health guidance like social distancing, or even incite panic. This carries over into all manner of visual choices that designers make. For example, color. Visualizations of Covid-19 cases and deaths have tended to use red bubbles or red-colored states and provinces. But color has cultural meaning — in Western cultures, it is used to indicate danger and harm. When a whole country is bathed in shades of red, or laden with red bubbles that obscure its boundaries, we need to be very careful about sensationalism. I’m not saying “never use red”; it is warranted in some cases to communicate the gravity of a situation. But our use of charged colors, particularly during a pandemic like this, involves making very careful ethical decisions. How serious is the risk to the individual reader? What do we want them to feel from viewing the visualization? What do we want them to do with the information in the visualization? Are those goals aligned with public health goals? The complexity of calculating a fatality rate in order to model the spread of Covid-19. From “Why It’s So Freaking Hard To Make A Good COVID-19 Model” by Maggie Koerth, Laura Bronner, and Jasmine Mithani for fivethirtyeight.com. Rather than reducing complexity (to generate sensationalist and attention-grabbing clicks), some of the most responsible visualization is working to explain the complexity behind our current crisis. This is the case in the above graphic. The journalists walk us through why even calculating a simple input like the fatality rate depends on many other variables, both known and unknown. All that said, public health communication really does need good visualization and data science right now. One of the exciting developments on the responsible-vis horizon is a new program from the Data Visualization Society that matches people with visualization skills to organizations working on Covid-19 that need their help. This is a great way to lend a hand, concretely, to the organizations who need help communicating their data during this crisis. Q: How can we as individuals best make sense of and put into context all the data being reported, almost by the minute, in some cases?A: One of my students said something wise to me this week. As she was describing her obsession with checking the news every couple minutes, she reflected, “I realized that I’m looking for answers that I cannot find, because nobody knows them.” She’s right. At this point, nobody can truly answer our most basic questions: “When will this end? Will I lose my job? When will my kids return to school? Are my loved ones safe? How will my community be changed by this?”No amount of data science or data visualization can solve these questions for us and give us the peace of mind we are craving. It is an inherently uncertain time, and we are in the middle of it. Rather than obsessively seeking information like case counts and scenario models to give us peace, I have been telling students to practice self-care and community-care as a way to direct their attention to things they have more control over. For example, in our local communities, Covid-19 is already having a disproportionate impact on the elderly, on health care workers, on first responders, on domestic workers, on single parents, on incarcerated people, and more. Below is one effective graphic that highlights these disproportionate impacts. From “Coronavirus quarantine: only one in four Americans can work from home” by Mona Chalabi for The Guardian.As the graphic shows, there is a great dimension of privilege in the people who are able to work from home: The vast majority of folks who can earn money from home are in the richest 25 percent of workers. This attention to how power and privilege play out unequally in data is also a throughline in Lauren F. Klein and my recently published book, “Data Feminism.” A feminist approach demands that we use data science and visualization to expose inequality and work toward equity.So while it is important to (responsibly) track and visualize death rates from Covid-19, how do we also focus our attention on efforts to support the groups who are most directly and unfairly impacted by this crisis, to get them the care, equipment and the economic security that they need? The reality — even amidst this great uncertainty — is that we can all take action now, in our local communities, to support each other. Assistant Professor Catherine D’Ignazio uses data and computational techniques to work toward gender and racial equity. MIT News spoke with her about the current boom in Covid-19 data visualizations, and how data visualizers can help us make sense of the pandemic’s uncertain numbers. Photo: Diana Levine https://news.mit.edu/2020/reducing-delays-wireless-networks-csail-0410 Congestion control system could help streaming video, mobile games, and other applications run more smoothly. Thu, 09 Apr 2020 23:59:59 -0400 https://news.mit.edu/2020/reducing-delays-wireless-networks-csail-0410 Rob Matheson | MIT News Office MIT researchers have designed a congestion-control scheme for wireless networks that could help reduce lag times and increase quality in video streaming, video chat, mobile gaming, and other web services.To keep web services running smoothly, congestion-control schemes infer information about a network’s bandwidth capacity and congestion based on feedback from the network routers, which is encoded in data packets. That information determines how fast data packets are sent through the network.Deciding a good sending rate can be a tough balancing act. Senders don’t want to be overly conservative: If a network’s capacity constantly varies from, say, 2 megabytes per second to 500 kilobytes per second, the sender could always send traffic at the lowest rate. But then your Netflix video, for example, will be unnecessarily low-quality. On the other hand, if the sender constantly maintains a high rate, even when network capacity dips, it could  overwhelm the network, creating a massive queue of data packets waiting to be delivered. Queued packets can increase the network’s delay, causing, say, your Skype call to freeze.Things get even more complicated in wireless networks, which have “time-varying links,” with rapid, unpredictable capacity shifts. Depending on various factors, such as the number of network users, cell tower locations, and even surrounding buildings, capacities can double or drop to zero within fractions of a second. In a paper at the USENIX Symposium on Networked Systems Design and Implementation, the researchers presented “Accel-Brake Control” (ABC), a simple scheme that achieves about 50 percent higher throughput, and about half the network delays, on time-varying links.The scheme relies on a novel algorithm that enables the routers to explicitly communicate how many data packets should flow through a network to avoid congestion but fully utilize the network. It provides that detailed information from bottlenecks — such as packets queued between cell towers and senders — by repurposing a single bit already available in internet packets. The researchers are already in talks with mobile network operators to test the scheme.“In cellular networks, your fraction of data capacity changes rapidly, causing lags in your service. Traditional schemes are too slow to adapt to those shifts,” says first author Prateesh Goyal, a graduate student in CSAIL. “ABC provides detailed feedback about those shifts, whether it’s gone up or down, using a single data bit.”Joining Goyal on the paper are Anup Agarwal, now a graduate student at Carnegie Melon University; Ravi Netravali, now an assistant professor of computer science at the University of California at Los Angeles; Mohammad Alizadeh, an associate professor in MIT’s Department of Electrical Engineering (EECS) and CSAIL; and Hari Balakrishnan, the Fujitsu Professor in EECS. The authors have all been members of the Networks and Mobile Systems group at CSAIL.Achieving explicit controlTraditional congestion-control schemes rely on either packet losses or information from a single “congestion” bit in internet packets to infer congestion and slow down. A router, such as a base station, will mark the bit to alert a sender — say, a video server — that its sent data packets are in a long queue, signaling congestion. In response, the sender will then reduce its rate by sending fewer packets. The sender also reduces its rate if it detects a pattern of packets being dropped before reaching the receiver.In attempts to provide greater information about bottlenecked links on a network path, researchers have proposed “explicit” schemes that include multiple bits in packets that specify current rates. But this approach would mean completely changing the way the internet sends data, and it has proved impossible to deploy. “It’s a tall task,” Alizadeh says. “You’d have to make invasive changes to the standard Internet Protocol (IP) for sending data packets. You’d have to convince all Internet parties, mobile network operators, ISPs, and cell towers to change the way they send and receive data packets. That’s not going to happen.”With ABC, the researchers still use the available single bit in each data packet, but they do so in such a way that the bits, aggregated across multiple data packets, can provide the needed real-time rate information to senders. The scheme tracks each data packet in a round-trip loop, from sender to base station to receiver. The base station marks the bit in each packet with “accelerate” or “brake,” based on the current network bandwidth. When the packet is received, the marked bit tells the sender to increase or decrease the “in-flight” packets — packets sent but not received — that can be in the network.If it receives an accelerate command, it means the packet made good time and the network has spare capacity. The sender then sends two packets: one to replace the packet that was received and another to utilize the spare capacity. When told to brake, the sender decreases its in-flight packets by one — meaning it doesn’t replace the packet that was received.Used across all packets in the network, that one bit of information becomes a powerful feedback tool that tells senders their sending rates with high precision. Within a couple hundred milliseconds, it can vary a sender’s rate between zero and double. “You’d think one bit wouldn’t carry enough information,” Alizadeh says. “But, by aggregating single-bit feedback across a stream of packets, we can get the same effect as that of a multibit signal.”Staying one step aheadAt the core of ABC is an algorithm that predicts the aggregate rate of the senders one round-trip ahead to better compute the accelerate/brake feedback.The idea is that an ABC-equipped base station knows how senders will behave — maintaining, increasing, or decreasing their in-flight packets — based on how it marked the packet it sent to a receiver. The moment the base station sends a packet, it knows how many packets it will receive from the sender in exactly one round-trip’s time in the future. It uses that information to mark the packets to more accurately match the sender’s rate to the current network capacity.In simulations of cellular networks, compared to traditional congestion control schemes, ABC achieves around 30 to 40 percent greater throughput for roughly the same delays. Alternatively, it can reduce delays by around 200 to 400 percent by maintaining the same throughput as traditional schemes. Compared to existing explicit schemes that were not designed for time-varying links, ABC reduces delays by half for the same throughput. “Basically, existing schemes get low throughput and low delays, or high throughput and high delays, whereas ABC achieves high throughput with low delays,” Goyal says.Next, the researchers are trying to see if apps and web services can use ABC to better control the quality of content. For example, “a video content provider could use ABC’s information about congestion and data rates to pick the resolution of streaming video more intelligently,” Alizadeh says. “If it doesn’t have enough capacity, the video server could lower the resolution temporarily, so the video will continue playing at the highest possible quality without freezing.” To reduce lag times and increase quality in video streaming, mobile gaming, and other web services, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have designed a congestion-control scheme for time-varying wireless links, such as cellular networks. Image: José-Luis Olivares, MIT https://news.mit.edu/2020/paradigm4-data-0405 Life science companies use Paradigm4’s unique database management system to uncover new insights into human health. Sat, 04 Apr 2020 23:59:59 -0400 https://news.mit.edu/2020/paradigm4-data-0405 Zach Winn | MIT News Office As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical “internet of things” devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data.But drawing meaningful conclusions from that data is a difficult problem that can involve piecing together different data types and manipulating huge data sets in response to varying scientific inquiries. The problem is as much about computer science as it is about other areas of science. That’s where Paradigm4 comes in.The company, founded by Marilyn Matz SM ’80 and Turing Award winner and MIT Professor Michael Stonebraker, helps pharmaceutical companies, research institutes, and biotech companies turn data into insights.It accomplishes this with a computational database management system that’s built from the ground up to host the diverse, multifaceted data at the frontiers of life science research. That includes data from sources like national biobanks, clinical trials, the medical internet of things, human cell atlases, medical images, environmental factors, and multi-omics, a field that includes the study of genomes, microbiomes, metabolomes, and more.On top of the system’s unique architecture, the company has also built data preparation, metadata management, and analytics tools to help users find the important patterns and correlations lurking within all those numbers.In many instances, customers are exploring data sets the founders say are too large and complex to be represented effectively by traditional database management systems.“We’re keen to enable scientists and data scientists to do things they couldn’t do before by making it easier for them to deal with large-scale computation and machine-learning on diverse data,” Matz says. “We’re helping scientists and bioinformaticists with collaborative, reproducible research to ask and answer hard questions faster.” A new paradigmStonebraker has been a pioneer in the field of database management systems for decades. He has started nine companies, and his innovations have set standards for the way modern systems allow people to organize and access large data sets.Much of Stonebraker’s career has focused on relational databases, which organize data into columns and rows. But in the mid 2000s, Stonebraker realized that a lot of data being generated would be better stored not in rows or columns but in multidimensional arrays.For example, satellites break the Earth’s surface into large squares, and GPS systems track a person’s movement through those squares over time. That operation involves vertical, horizontal, and time measurements that aren’t easily grouped or otherwise manipulated for analysis in relational database systems.Stonebraker recalls his scientific colleagues complaining that available database management systems were too slow to work with complex scientific datasets in fields like genomics, where researchers study the relationships between population-scale multi-omics data, phenotypic data, and medical records.“[Relational database systems] scan either horizontally or vertically, but not both,” Stonebraker explains. “So you need a system that does both, and that requires a storage manager down at the bottom of the system which is capable of moving both horizontally and vertically through a very big array. That’s what Paradigm4 does.”In 2008, Stonebraker began developing a database management system at MIT that stored data in multidimensional arrays. He confirmed the approach offered major efficiency advantages, allowing analytical tools based on linear algebra, including many forms of machine learning and statistical data processing, to be applied to huge datasets in new ways.Stonebraker decided to spin the project into a company in 2010, when he partnered with Matz, a successful entrepreneur who co-founded Cognex Corporation, a large industrial machine-vision company that went public in 1989. The founders and their team, including Alex Poliakov BS ’07, went to work building out key features of the system, including its distributed architecture that allows the system to run on low-cost servers, and its ability to automatically clean and organize data in useful ways for users.The founders describe their database management system as a computational engine for scientific data, and they’ve named it SciDB. On top of SciDB, they developed an analytics platform, called the REVEAL discovery engine, based on users’ daily research activities and aspirations.“If you’re a scientist or data scientist, Paradigm’s REVEAL and SciDB products take care of all the data wrangling and computational ‘plumbing and wiring,’ so you don’t have to worry about accessing data, moving data, or setting up parallel distributed computing,” Matz says. “Your data is science-ready. Just ask your scientific question and the platform orchestrates all of the data management and computation for you.”SciDB is designed to be used by both scientists and developers, so users can interact with the system through graphical user interfaces or by leveraging statistical and programming languages like R and Python.“It’s been very important to sell solutions, not building blocks,” Matz says. “A big part of our success in the life sciences with top pharmas and biotechs and research institutes is bringing them our REVEAL suite of application-specific solutions to problems. We’re not handing them an analytical platform that’s a set of Spark LEGO blocks; we’re giving them solutions that handle the data they deal with daily, and solutions that use their vocabulary and answer the questions they want to work on.”Accelerating discoveryToday Paradigm4’s customers include some of the biggest pharmaceutical and biotech companies in the world as well as research labs at the National Institutes of Health, Stanford University, and elsewhere.Customers can integrate genomic sequencing data, biometric measurements, data on environmental factors, and more into their inquiries to enable new discoveries across a range of life science fields.Matz says SciDB did 1 billion linear regressions in less than an hour in a recent benchmark, and that it can scale well beyond that, which could speed up discoveries and lower costs for researchers who have traditionally had to extract their data from files and then rely on less efficient cloud-computing-based methods to apply algorithms at scale.“If researchers can run complex analytics in minutes and that used to take days, that dramatically changes the number of hard questions you can ask and answer,” Matz says. “That is a force-multiplier that will transform research daily.”Beyond life sciences, Paradigm4’s system holds promise for any industry dealing with multifaceted data, including earth sciences, where Matz says a NASA climatologist is already using the system, and industrial IoT, where data scientists consider large amounts of diverse data to understand complex manufacturing systems. Matz says the company will focus more on those industries next year.In the life sciences, however, the founders believe they already have a revolutionary product that’s enabling a new world of discoveries. Down the line, they see SciDB and REVEAL contributing to national and worldwide health research that will allow doctors to provide the most informed, personalized care imaginable.“The query that every doctor wants to run is, when you come into his or her office and display a set of symptoms, the doctor asks, ‘Who in this national database has genetics that look like mine, symptoms that look like mine, lifestyle exposures that look like mine? And what was their diagnosis? What was their treatment? And what was their morbidity?” Stonebraker explains. “This is cross correlating you with everybody else to do very personalized medicine, and I think this is within our grasp.” Paradigm4 allows users to integrate data from sources like genomic sequencing, biometric measurements, environmental factors, and more into their inquiries to enable new discoveries across a range of life science fields. https://news.mit.edu/2020/paradigm4-data-0405 Life science companies use Paradigm4’s unique database management system to uncover new insights into human health. Sat, 04 Apr 2020 23:59:59 -0400 https://news.mit.edu/2020/paradigm4-data-0405 Zach Winn | MIT News Office As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical “internet of things” devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data.But drawing meaningful conclusions from that data is a difficult problem that can involve piecing together different data types and manipulating huge data sets in response to varying scientific inquiries. The problem is as much about computer science as it is about other areas of science. That’s where Paradigm4 comes in.The company, founded by Marilyn Matz SM ’80 and Turing Award winner and MIT Professor Michael Stonebraker, helps pharmaceutical companies, research institutes, and biotech companies turn data into insights.It accomplishes this with a computational database management system that’s built from the ground up to host the diverse, multifaceted data at the frontiers of life science research. That includes data from sources like national biobanks, clinical trials, the medical internet of things, human cell atlases, medical images, environmental factors, and multi-omics, a field that includes the study of genomes, microbiomes, metabolomes, and more.On top of the system’s unique architecture, the company has also built data preparation, metadata management, and analytics tools to help users find the important patterns and correlations lurking within all those numbers.In many instances, customers are exploring data sets the founders say are too large and complex to be represented effectively by traditional database management systems.“We’re keen to enable scientists and data scientists to do things they couldn’t do before by making it easier for them to deal with large-scale computation and machine-learning on diverse data,” Matz says. “We’re helping scientists and bioinformaticists with collaborative, reproducible research to ask and answer hard questions faster.” A new paradigmStonebraker has been a pioneer in the field of database management systems for decades. He has started nine companies, and his innovations have set standards for the way modern systems allow people to organize and access large data sets.Much of Stonebraker’s career has focused on relational databases, which organize data into columns and rows. But in the mid 2000s, Stonebraker realized that a lot of data being generated would be better stored not in rows or columns but in multidimensional arrays.For example, satellites break the Earth’s surface into large squares, and GPS systems track a person’s movement through those squares over time. That operation involves vertical, horizontal, and time measurements that aren’t easily grouped or otherwise manipulated for analysis in relational database systems.Stonebraker recalls his scientific colleagues complaining that available database management systems were too slow to work with complex scientific datasets in fields like genomics, where researchers study the relationships between population-scale multi-omics data, phenotypic data, and medical records.“[Relational database systems] scan either horizontally or vertically, but not both,” Stonebraker explains. “So you need a system that does both, and that requires a storage manager down at the bottom of the system which is capable of moving both horizontally and vertically through a very big array. That’s what Paradigm4 does.”In 2008, Stonebraker began developing a database management system at MIT that stored data in multidimensional arrays. He confirmed the approach offered major efficiency advantages, allowing analytical tools based on linear algebra, including many forms of machine learning and statistical data processing, to be applied to huge datasets in new ways.Stonebraker decided to spin the project into a company in 2010, when he partnered with Matz, a successful entrepreneur who co-founded Cognex Corporation, a large industrial machine-vision company that went public in 1989. The founders and their team, including Alex Poliakov BS ’07, went to work building out key features of the system, including its distributed architecture that allows the system to run on low-cost servers, and its ability to automatically clean and organize data in useful ways for users.The founders describe their database management system as a computational engine for scientific data, and they’ve named it SciDB. On top of SciDB, they developed an analytics platform, called the REVEAL discovery engine, based on users’ daily research activities and aspirations.“If you’re a scientist or data scientist, Paradigm’s REVEAL and SciDB products take care of all the data wrangling and computational ‘plumbing and wiring,’ so you don’t have to worry about accessing data, moving data, or setting up parallel distributed computing,” Matz says. “Your data is science-ready. Just ask your scientific question and the platform orchestrates all of the data management and computation for you.”SciDB is designed to be used by both scientists and developers, so users can interact with the system through graphical user interfaces or by leveraging statistical and programming languages like R and Python.“It’s been very important to sell solutions, not building blocks,” Matz says. “A big part of our success in the life sciences with top pharmas and biotechs and research institutes is bringing them our REVEAL suite of application-specific solutions to problems. We’re not handing them an analytical platform that’s a set of Spark LEGO blocks; we’re giving them solutions that handle the data they deal with daily, and solutions that use their vocabulary and answer the questions they want to work on.”Accelerating discoveryToday Paradigm4’s customers include some of the biggest pharmaceutical and biotech companies in the world as well as research labs at the National Institutes of Health, Stanford University, and elsewhere.Customers can integrate genomic sequencing data, biometric measurements, data on environmental factors, and more into their inquiries to enable new discoveries across a range of life science fields.Matz says SciDB did 1 billion linear regressions in less than an hour in a recent benchmark, and that it can scale well beyond that, which could speed up discoveries and lower costs for researchers who have traditionally had to extract their data from files and then rely on less efficient cloud-computing-based methods to apply algorithms at scale.“If researchers can run complex analytics in minutes and that used to take days, that dramatically changes the number of hard questions you can ask and answer,” Matz says. “That is a force-multiplier that will transform research daily.”Beyond life sciences, Paradigm4’s system holds promise for any industry dealing with multifaceted data, including earth sciences, where Matz says a NASA climatologist is already using the system, and industrial IoT, where data scientists consider large amounts of diverse data to understand complex manufacturing systems. Matz says the company will focus more on those industries next year.In the life sciences, however, the founders believe they already have a revolutionary product that’s enabling a new world of discoveries. Down the line, they see SciDB and REVEAL contributing to national and worldwide health research that will allow doctors to provide the most informed, personalized care imaginable.“The query that every doctor wants to run is, when you come into his or her office and display a set of symptoms, the doctor asks, ‘Who in this national database has genetics that look like mine, symptoms that look like mine, lifestyle exposures that look like mine? And what was their diagnosis? What was their treatment? And what was their morbidity?” Stonebraker explains. “This is cross correlating you with everybody else to do very personalized medicine, and I think this is within our grasp.” Paradigm4 allows users to integrate data from sources like genomic sequencing, biometric measurements, environmental factors, and more into their inquiries to enable new discoveries across a range of life science fields. https://news.mit.edu/2020/mit-entrepreneur-innovation-covid-19-0402 Entrepreneurial groups around the Institute have launched initiatives to address challenges brought on by the Covid-19 pandemic. Thu, 02 Apr 2020 10:52:05 -0400 https://news.mit.edu/2020/mit-entrepreneur-innovation-covid-19-0402 Zach Winn | MIT News Office Innovation and entrepreneurship aren’t easy. New companies are forced to make due with minimal resources. Decisions must be made in the face of great uncertainty. Conditions change rapidly.Perhaps unsurprisingly then, MIT’s I&E community has stepped up to the unforeseen challenges of the Covid-19 pandemic. Groups from many corners of the Institute are adapting to the myriad disruptions brought on by the emergency and spearheading efforts to help the people most affected.At a time when most students would be on spring break, many were collaborating on projects and participating in hacking workshops to respond to Covid-19. And as faculty and staff develop new curricula and support structures, they’re focusing on the needs of their students with the same devotion entrepreneurs must focus on their customers.Above all, members of the MIT community have treated the challenges presented by Covid-19 as opportunities to help. Perhaps nowhere is that more apparent than the Covid-19 Rapid Innovation Dashboard, which was just a rough idea as recently as March 16, but is now a bustling hub of MIT’s Covid-19-related activities. Projects on the dashboard include an initiative to help low-income K-12 students with school shutdowns, an effort leveraging behavioral science to reduce the spread of misinformation about the virus, and multiple projects aimed at improving access to ventilators.People following those projects would hardly suspect the participants have been uprooted from their lives and forced to radically change the way they work.“We never would’ve wished this on anybody, but I feel like we’re ready for it,” says Bill Aulet, the managing director of the Martin Trust Center for MIT Entrepreneurship and a professor of the practice at MIT’s Sloan School of Management. “Working in an environment of great change, if you’re a great entrepreneur, is playing to your strengths. I think the students will rise to the occasion, and that’s what we’re seeing now.”The Rapid Innovation DashboardIn the second week of March, as the global consequences of Covid-19’s spread were becoming apparent, members of the MIT Innovation Initiative began getting contacted by members of the MIT community looking for ways to help.Most people wanted information on the various grassroots projects that had sprouted up around campus to address disruptions related to the spread of the virus. Some people were looking for ways to promote their projects and get support.MITii’s team began brainstorming ways to help fill in those gaps, officially beginning work on the dashboard the week of March 16 — the same time staff members began working remotely.“From ideation to whiteboarding, to concept, to iteration, to launch, we did it all in real time, and we went from idea to standing the dashboard up in four days,” MITii executive director Gene Keselman says. “It was beautiful for all of us innovation nerds.”The site launched on March 19 with six projects. Today there are 50 live projects on the site and counting. Some of them deal with mechanical or scientific problems, like the aforementioned efforts to improve access to ventilators, while others are more data-focused, like an initiative to track the spread of the virus at the county level. Still others are oriented toward wellness, like a collection of MIT-related coloring pages for destressing.“A lot of the things we’re seeing are data-driven, creative-driven projects to get people involved and get them feeling like they’re making an impact,” Keselman says.The current dashboard is version 1.0 of an ongoing project that will continue to evolve based on the community’s needs. Down the line, the MITii team is considering ways to better connect the MIT community with investors looking to fund projects related to the virus.“This is going to be a long term problem, and even when we go back to the office, issues will persist, we’ll be dealing with things that are the runoff from Covid-19,” Keselman says. “There will be an opportunity to keep this thing going to solve all kinds of second- or third-order problems.”Overcoming adversityThe dashboard is just one example of how different entrepreneurial organizations on campus are stepping up to the challenges of Covid-19. The Trust Center is encouraging students to leverage its Orbit app, to get help from entrepreneurs in residence, engage with other members of MIT’s entrepreneurial community, and navigate MIT’s myriad entrepreneurial resources. And in response to Covid-19, the Trust Center launched the Antifragile Entrepreneurship Speaker Series to provide thought leadership to students.“We’ve revitalized our speaker series,” Aulet says. “We used to fly people in, but now we can have anyone. They’re sitting at home, they’re bored, and we can have more interaction than we did before. We try to create antifragile humans, and antifragile humans excel in times like this.”MIT D-Lab, where hands-on learning and common makerspaces are central to operations, is just one example of an area where faculty members are taking this opportunity to try new ways of managing projects and rethinking their curriculum.“We’re in a real brainstorming phase right now, in the best sense of the word — throwing out all the wild ideas that come to us, and entertaining anything as we decide how to move forward,” Libby Hsu, a lecturer and academic program manager at D-Lab, told MIT News the week before MIT classes resumed. “We’re getting ready to ship materials and tools to students at their homes. We’re studying how to use Zoom to facilitate project work student teams have already put in. We’re realistically re-assessing what deliverables we could ask of students to help D-Lab staff prototype things for them here on campus, perhaps later in the semester or over the summer.”Other entrepreneurial groups on campus, like the Venture Mentoring Service, MIT Sandbox, and the Legatum Center, are similarly adopting virtualized support mechanisms.On March 5, MIT Solve, which uses social impact challenges to tackle the world’s biggest problems, launched a new Global Challenge seeking innovations around the prevention, detection, and response of Covid-19. The winning team will receive a $10,000 grant to further develop their solution.The students themselves, of course, are also organizing initiatives. In addition to the student-led projects in the Rapid Innovation Dashboard, student finalists in this year’s MIT IDEAS Social Innovation Challenge have leveraged their entrepreneurial experience to help address equipment shortages and assist communities in fulfilling changing food and shelter needs. And a new community initiative, the MIT COVID-19 Challenge, held its first virtual “ideathon” this past weekend, with another major event April 3-5.Indeed, Keselman could’ve been talking about any group on campus when he said of his team at MITii, “We feel like we lived an entire lifetime in just the last week.”The early efforts may not have been the way many participants expected to spend their spring break, but in the entrepreneurial world, new challenges are par for the course.“Being knocked out of your homeostasis is a good thing and a bad thing, and it’s an entrepreneur’s job to make it more of a good thing than a bad thing,” Aulet says. “I think we’ll come out of this utilizing technology to have more efficient, more effective, more inclusive engagements. Is this disrupting the entrepreneurial ecosystem? Absolutely. Should we come out of it stronger? Absolutely.” MIT’s innovation and entrepreneurship (I&E) community has stepped up to the challenges presented by the Covid-19 pandemic — and perhaps nowhere is that more apparent than in the new Covid-19 Rapid Innovation Dashboard, pictured, which features more than 50 projects, research initiatives, startups, and global activities undertaken by the MIT community to respond to the crisis. Image: Christine Daniloff, MIT https://news.mit.edu/2020/2020-us-census-students-0331 Amid disruptions caused by the Covid-19 outbreak, the MIT community has an important role to play in the 2020 census. Tue, 31 Mar 2020 00:00:00 -0400 https://news.mit.edu/2020/2020-us-census-students-0331 Zach Winn | MIT News Office The year’s U.S. census is taking place at a unique time in the country’s history. Many people, including college students, are staying in their homes as a result of the Covid-19 pandemic. As a result, the Census Bureau has taken a number of steps to respond to the disruptions of the outbreak.Students who are usually at school should be counted at school, even if they are temporarily living somewhere else due to the Covid-19 pandemic, and universities like MIT are working with census officials to count students that normally live in a dorm or other college-owned housing.But, under official guidance, “if you live in an apartment or house alone or with roommates or others,” you should receive an invitation in the mail to respond to the census, which you can respond to online, by phone, or by mail. “Whatever method you choose,” the guidance continues, “make sure you use your normal address — where you usually live while you’re at school. You should also include anyone else who normally lives there, too. That means you’ll be asked about your roommates’ birthdays, how they want to identify their race, etc. But if you don’t know that information, or you can’t verify whether your roommate has already responded for your home, please respond for the entire household.”Census Day is April 1, but the government strongly encourages online responses, which can be submitted here until Aug. 14 under a revised schedule. Census takers will also follow up with some households that don’t respond. Still, most things will not change for the once-a-decade-survey. By law, the Census Bureau must deliver each state’s population total to the president by Dec. 31 of this year. That’s because census data have important implications for redistricting and representation purposes.The census is valuable for a number of other applications as well. To learn more, and to understand why members of the MIT community should participate, MIT News spoke with Melissa Nobles, the Kenan Sahin Dean of the School of Humanities, Arts, and Social Sciences, and Amy Glasmeier, a professor in the Department of Urban Studies and Planing, both of whom have used census data for important research throughout their careers.Q: Why is the census so important?GLASMEIER: The census is the basis of many important functions in our society. First, it helps to set the congressional districts and decide how many representatives particular geographic areas have. Second, the census is used to determine the distribution of federal resources. For example, if a region goes down to 49,000 people, it’s not considered a metropolitan area anymore and falls into a completely different [resource allocation] category. Third, it’s important at the community level. Communities are responsible for certain kinds of goods and services, and if they don’t have an accurate count of their population, they don’t have a good way of knowing what their responsibilities are. It’s incredibly important to know how many students are in your school district and the growth rate of your school district, or the growth rate of your elderly population. So the census is the statistical fabric, if you will, of our society.NOBLES: Over the centuries, the importance of census data has grown far past representative purposes. Uses now extend to budgeting and really anything we care about in public life.The census deals with many things researchers are interested in. From where people live to how they are living, to how large their households are, to age distribution, gender distribution, etc. It’s a public service and it allows for broad access to data by researchers, which is different from private databases ,which may not provide you that information. It’s a public service that researchers rely on enormously.Q: Why should members of the MIT community participate?NOBLES: The census is based on inhabitants in locations, so it’s indifferent to citizenship. It’s important for governments (federal, state, and municipal) and researchers to know that international students are here, for example, and how many people there are in their communities.The main thrust of the census is to be counted. It asks where every inhabitant in the U.S. is on April 1, census day. It’s a relatively quick survey and it’s worth doing; it’s part of our civic duty. Our government needs reliable data — we should appreciate the importance of that, at MIT. In order to make good policies, you first need good data, so participating in the census is  part of our intellectual duty in addition to our civic duty.GLASMEIER: Unless someone is registered to vote in their home, they’re going to be identified here as a resident in a group quarter. This kind of information is important for the city of Cambridge, because they’re making decisions about things like water supply, housing, and transportation, and it’s also important from the perspective of understanding who’s going to college. What’s their personal history? Where do they come from, from the standpoint of ethnicity, race, gender?Q: What do you wish more people know about the census?NOBLES: I don’t want people to be suspicious of it. There are rightly many concerns right now about data privacy, and sometimes it seems people are more fearful of the census than they are of private corporations, which often have way more personal information than the government, by the way. You can rest assured that these data are used for a range of government programs, most importantly our own democratic governance, and it’s part of living in the U.S. People should look at it as a useful tool and not be suspicious of it.GLASMEIER: The anonymity is important. America is extremely rigorous about confidentiality across the entire census. It also sets the political environment for the nation, and it’s exceedingly important in that way. Finally, for those of us who use it for research purposes, it’s a daily thing we touch. For many others that are starting to deal with populations and think about people, the census is this amazing source they may not even know exists. Census Day is April 1, but the government strongly encourages online responses, which can be submitted until Aug. 14 under a revised schedule. https://news.mit.edu/2020/simchi-levi-supply-chain-covid-19-0325 MIT Professor David Simchi-Levi forecast the mid-March manufacturing pause. Now he looks ahead. Wed, 25 Mar 2020 12:26:21 -0400 https://news.mit.edu/2020/simchi-levi-supply-chain-covid-19-0325 Peter Dizikes | MIT News Office With the Covid-19 virus disrupting the global economy, what is the effect on the international supply chain? In a pair of articles, MIT News examines core supply-chain issues, in terms of affected industries and the timing of unfolding interruptions.The rapid spread of the Covid-19 virus is already having a huge impact on the global economy, which is rippling around the world via the long supply chains of major industries.MIT supply chain expert David Simchi-Levi has been watching those ripples closely in 2020, as they have moved from China outward to the U.S. and Europe. His tracking of supply chain problems provides insight into what is happening in the global economy — and what could happen in a variety of scenarios.“This is a significant challenge,” says Simchi-Levi, who is a professor of engineering systems in the School of Engineering and in the Institute for Data, Systems, and Society within the MIT Stephen A. Schwarzman College of Computing. The global public health crisis, he adds, “is not only affecting the supply chain. There is a significant impact on demand, and as a result, a significant impact on the financial performance on all these businesses.”In late February, Simchi-Levi and technology executive Pierre Haren issued a short-term forecast, in a Harvard Business Review blog, that the effects of Covid-19 in China would lead to a manufacturing slowdown in the U.S. and Europe by mid-March. Why? Because many Chinese manufacturing suppliers temporarily, but drastically, cut production in late January, due to the problems caused by the virus. Given the timetable of global shipping, that meant major manufacturers would be low on supplies about six weeks later.“The last shipment to deliver products arrived at the end of February, because it takes about 30 days from Asia to North America or Europe,” says Simchi-Levi. “Most manufacturing companies will keep about two weeks of inventory. That means they were able to cover demand for the first two weeks of March. After that, [given] no more supplies, there will be disruption of manufacturing. And we see this right now.”Indeed, most major automakers have cut production in the days since mid-March, with Volkswagen and Renault shuttering facilities in Europe as a result. In the U.S., most major automakers announced last week they were temporarily closing plants due to health and safety concerns, with Honda explicitly stating on March 18 that they would enact a temporary shutdown “due to an anticipated decline in market demand related to the economic impact of the Covid-19 pandemic,” as well as in response to health concerns.“Our prediction that was made in the last week of February has been realized now,” Simchi-Levi says. “Automotive manufacturing companies are closing facilities. Not only are they reducing production — they are closing facilities.”However, China’s public health situation has been improving in recent weeks, with a reduction in the rate of new Covid-19 cases and fatalities. If the public health situation in the U.S. and Europe did not worsen, then a return to something like normal manufacturing output would be conceivable by the end of the second quarter, Simchi-Levi says.Looking at survey data from over 3,000 suppliers in China, Simchi-Levi notes that about half of employees at Chinese suppliers were not anticipating being at full capacity until the end of March. The remaining suppliers anticipate a much longer recovery to full capacity.“These suppliers are starting to come back,” Simchi-Levi says, noting that in the best-case scenario, “We will not see significant volume [from China] before the end of April.”However, with the U.S. and many European countries continuing to see a rapid increase in Covid-19 cases and deaths throughout March, and curtailing social and economic activity, the best-case scenario seems unlikely.“At the beginning the problem was reduction in supply,” says Simchi-Levi, referring to China’s production issues. “But now it is not only a matter of supply from China; it is supply from everywhere. Second, the demand side is completely changing. Now there is a big drop in demand.” With unemployment rising and consumer spending reduced, fewer large manufacturers can keep operating at full capacity or anything close to it.“This also implies a third element, which is the financial impact on companies,” Simchi-Levi says. “It’s not just disruption in the supply chain; the significant drop in demand has a significant financial impact.” So, even if China and other Asian countries rebound relatively quickly, businesses in the U.S. and Europe might not have the capacity to ramp up production and rehire workers at their previous levels.“You can see there are cascading effects that have an impact,” Simchi-Levi says. Clearly, without an abatement of the spread of Covid-19, a substantial economic uptick is hard to imagine.“This is all fluid and dynamic,” he warns.For more on the connection between China and global manufacturing, see our other story about the Covid-19 crisis and supply chain. “Most manufacturing companies will keep about two weeks of inventory,” says David Simchi-Levi, who is a professor of engineering systems in the School of Engineering and in the Institute for Data, Systems, and Society within the MIT Stephen A. Schwarzman College of Computing. “That means they were able to cover demand for the first two weeks of March. After that, [given] no more supplies, there will be disruption of manufacturing. And we see this right now.” https://news.mit.edu/2020/accessible-designs-data%20visualization-0313 An MIT team discusses the pitfalls of “parachute research” and the importance of “sociotechnical” factors. Fri, 13 Mar 2020 00:00:00 -0400 Rob Matheson https://news.mit.edu/2020/accessible-designs-data%20visualization-0313 https://news.mit.edu/2020/events-postponed-canceled-covid-19-0309 Changes follow new Institute policies on travel, events, and visitors; some large classes to move online. Mon, 09 Mar 2020 14:48:39 -0400 https://news.mit.edu/2020/events-postponed-canceled-covid-19-0309 MIT News Office MIT schools, departments, labs, centers, and offices have acted swiftly to postpone or cancel large events through May 15 in the wake of the Institute’s announcement last week of new policies regarding gatherings likely to attract 150 or more people.To safeguard against COVID-19, and the spread of the 2019 novel coronavirus, many other MIT events have been modified both on campus and elsewhere, with increased opportunities offered for livestreaming.The guidelines put forth last week have also now been expanded to include some large classes: The Institute will move classes with more than 150 students online, starting this week.Impacts on classes and student travelFollowing consultation with senior academic leadership and experts within MIT Medical, the Institute has suspended in-person meetings of classes with more than 150 students, effective tomorrow, Tuesday, March 10. The approximately 20 classes impacted by the decision will continue to be offered in virtual form.“We are being guided by our medical professionals who are in close contact with state and national public health officials,” Ian Waitz, vice chancellor for undergraduate and graduate education, wrote today in a letter to deans and department heads. “They have advised us that while the risk to the community is low and there are no cases on campus as of now, we need to move quickly to help prevent the potential transmission of the disease and to be ready if and when it impacts our campus.”“Our approach is to be aggressive, but to move forward in stages,” Waitz added, “while keeping in mind that some individual faculty and departments may be moving faster than others, that the level of comfort with remote teaching varies, and that some classes may translate better than others to alternative formats.”As of now, midterm examinations will proceed as scheduled, but the plan for large courses is to run midterms in several rooms simultaneously so the number of students in each room remains well below 150. The Registrar’s Office is working on room scheduling strategies to best accommodate that approach.  The Institute has also decided that all MIT-sponsored student domestic travel of more than 100 miles will have to go through the Institute’s high-risk travel waiver process. Impacts on undergraduate and graduate admissionsAs shared in President L. Rafael Reif’s letter of last Thursday, MIT’s new policy on events will apply to Campus Preview Weekend, ordinarily an on-campus gathering for students admitted to the incoming first-year undergraduate class. In the coming weeks, the Admissions Office will be connecting with admitted students, current students, and campus partners to discuss what to do instead of a conventional CPW. For more information, please see: https://mitadmissions.org/blogs/entry/mits-covid-19-precautions-and-its-impact-on-admissions/The Admissions Office will not host any programming for K-12 students, including admitted students and their families, between now and May 15, regardless of the size of the event. All scheduled admissions sessions and tours have been canceled between now and May 15, and MIT Admissions is canceling all scheduled admissions officer travel to domestic and international events in that time window. Additionally, all graduate admissions visit days have been canceled, effective immediately. “Based upon reducing risk, we ask all departments to cancel all remaining graduate open houses and visit days, and to move to virtual formats,” Waitz says. “Many departments have already done this.”Despite the cancellation of these formal events, the MIT campus currently remains open for visits by prospective students. However, in keeping with suggested best practices for public health, visitors from countries that the U.S. Centers for Disease Control and Prevention (CDC) finds have “widespread sustained (ongoing) transmission” of COVID-19 cannot visit campus until they have successfully completed 14 days of self-quarantine.Impacts on major campus eventsThe MIT Excellence Awards and Collier Medal celebration, scheduled for this Thursday, March 12, has been postponed; a rescheduled date will be announced as soon as it is confirmed. The Excellence Awards and Collier Medal recognize the work of service, support, administrative, and sponsored research staff. The Excellence Awards acknowledge the extraordinary efforts made by members of the MIT community toward fulfilling the goals, values, and mission of the Institute. The Collier Medal is awarded to an individual or group exhibiting qualities such as a commitment to community service, kindness, selflessness, and generosity; it honors the memory of MIT Police Officer Sean Collier, who lost his life while protecting the MIT campus. A full list of this year’s honorees is available.Career Advising and Professional Development is working on plans to change the format of the Spring Career Fair, previously scheduled for April 2, to a virtual career fair for a date to be announced in April. All other large-scale employer engagement events — such as career fairs, mixers, symposiums, and networking events — will also be canceled; adopt a virtual model; be postponed beyond May 15; or adopt other models that meet the new policies involving large events. MIT is postponing the remaining two Climate Action Symposia, “MIT Climate Initiatives and the Role of Research Universities” and “Summing Up: Why Is the World Waiting?” — previously scheduled for April 2 and April 22, respectively. These symposia will be rescheduled; new dates will be announced on climatesymposia.mit.edu. Solve at MIT on May 12-14 will be virtual. In addition to a livestream on this page, Solve will continue to bring together its cross-sector community via interactive online workshops and more. Participants can also contribute a solution or a donation to the Health Security and Pandemics Challenge.Impacts on athletics and intercollegiate athletics eventsThe Department of Athletics, Physical Education and Recreation (DAPER) is taking steps to safeguard student-athletes, staff, and community members who utilize DAPER facilities for club sports, intramurals, and recreation. Unless otherwise announced, MIT’s intercollegiate athletics events will continue as scheduled. However, visiting teams are asked to bring only student-athletes and essential team personnel to events at MIT. Additionally, DAPER has requested that only MIT students, faculty, and staff members attend upcoming home athletic events through May 15. All other spectators, including parents, are asked to watch events using DAPER’s video streaming service.Other impacted events and activitiesDiscussions are ongoing about many additional events scheduled between now and May 15. The list below will be updated as more information becomes available. Among the affected events and activities announced so far: Use of the pillars in Lobby 7 for community discussion is suspended for the rest of the spring semester, to minimize close contact and sharing of writing implements. The Advanced Undergraduate Research Opportunities Program — better known as SuperUROP — has canceled the SuperUROP Showcase poster session, which had been scheduled for April 23, 2020. The SuperUROP teaching staff will be in touch with SuperUROP scholars about working remotely to fulfill the research and academic requirements for this program. SuperUROP has also canceled the program’s closing reception, which had been scheduled for May 7, 2020. The SuperUROP logistics team will be in touch with SuperUROP scholars about how to obtain their certificates for completing the program. Questions? Email superurop-contact@mit.edu. SpaceTech 2020, scheduled for Wednesday, March 11, has been postponed until a later date. The all-day event, part of MIT Space Week, will highlight the future of space exploration by featuring lightning talks from current students; talks and panels from alumni; and an interactive guided tour along the Space Trail to visit Department of Aeronautics and Astronautics (AeroAstro) labs and ongoing research projects. Visit spacetech.mit.edu for the latest information. MIT Getfit has canceled both of its midpoint events originally scheduled for Wednesday, March 11. Organizers are working to contact participants with more information. The March 13 lecture titled “Fateful Triangle: How China Shaped US-India Relations During the Cold War,” by Tanvi Madan of the Brookings Institution, has been postponed. More information is available at http://southasianpolitics.net/. To the Moon to Stay Hackathon, scheduled for Saturday, March 14, has been postponed until a later date. MIT AeroAstro and the MIT Media Lab’s Space Exploration Initiative are partnering to design and build an experiment to go to the moon on board Blue Origin’s inaugural lunar mission. The goal of the hackathon is to bring the MIT community together to think about lunar missions and habitation through a variety of challenges. To receive updates, join their email list or visit tothemoon.mit.edu. The Koch Institute is limiting attendance at the SCIENCE with/in/sight: 2020 Visions event on March 17. This event is now for invited guests only. All MIT Communications Forum events have been postponed until the fall. This includes Science Under Attack, originally scheduled for March 19, and David Thorburn’s presentation as part of the William Corbett Poetry Series, originally scheduled for April 8. The MIT de Florez Award Competition, scheduled for April 15, will be conducted virtually. Additional information will be sent to the Mechanical Engineering community via email.  The Mechanical Engineering Graduate Student Gala, scheduled for April 19, has been canceled and will be rescheduled for the fall. The Mechanical Engineering Student Awards Banquet, scheduled for May 15, has been canceled. Awards will be announced virtually. The Office of Engineering Outreach Programs (OEOP) has canceled its SEED Academy program through May 15. This includes the SEED Academy Spring Final Symposium on May 9. OEOP will continue to communicate with SEED Academy students and parents via email and through The Sprout newsletter to offer information on course, project, and engagement options. The 2020 Brazil Conference at MIT and Harvard has been canceled. More information can be found at brazilconference.org. The March 12 Starr Forum, titled “Russia’s Putin: From Silent Coup to Legal Dictatorship,” has been changed to a live webcast. The March 13 Myron Weiner Seminar on International Migration, titled “Future Aspirations Among Refugee Youth in Turkey Between Integration & Mobility,” has been canceled. The MIT Sloan School of Management is canceling all international study tours and treks. Student conferences are either being cancelled or modified: The March 7 Robo-AI Exchange Conference, the March 13 New Space Age Conference, and the April 2 Golub Center for Finance and Policy discussion on equity market structure with the SEC are canceled. The March 13 ETA Summit and the April 17 Ops Sim Competition are proceeding, with virtualization. The March 16 Entrepreneurship and Innovation Alumni gathering in San Franciso is also canceled. The 2020 MIT Scholarship and UROP Brunch that was scheduled for April 4 has been canceled. The MIT Campaign for a Better World event in Toronto, originally set for April 29, will be postponed. The Program in Science, Technology, and Society’s Morison Lecture and Prize in Science, Technology, and Society, originally scheduled for April 14, 2020, 4 p.m.; E51-Wong Auditorium, has been rescheduled for Oct. 1, 2020. The Women’s and Gender Studies Program’s Women Take the Reel Series film event,”Warrior Women,” scheduled for March 12 at 6:30 p.m., has been postponed until fall 2020. The MIT Graduate Alumni Gathering, scheduled for March 20–21 in Cambridge, has been postponed, with plans for rescheduling to a later date in 2021. The MIT Student Alumni Association’s Dinner with 12 Strangers event series, set to be held in Cambridge and Boston, has been cancelled for the spring semester. The Forum on the Future of Cities: Urban Climate hosted by the Senseable City Lab and the World Economic Forum, scheduled for April 6th, will be postponed until the Fall of 2020, with a date to be decided later. Digital Humanities @ MIT, scheduled for March 12, has been postponed until a date to be announced. The MacVicar Day symposium and reception have been canceled. The MIT Statistics and Data Science Conference (SDSCon 2020) scheduled for April 3, has been postponed to fall 2020. More information can be found at sdscon.mit.edu. J-PAL North America’s Fourth Annual State and Local Innovation Initiative Convening: Charting the Next Decade of Evidence Generation in State and Local Governments has been postponed. The event was originally scheduled to take place March 19-20 at MIT and will now be rescheduled for November 2020. The Department of Electrical Engineering and Computer Science (EECS) has canceled the SuperUROP information session that had been scheduled for Wednesday, March 11, from 4-5 p.m. A video of the March 3 information session will be available shortly on the SuperUROP home page. The Department of Electrical Engineering and Computer Science has canceled Masterworks, the April 23 EECS poster session showcasing research by current and recent master’s students. There are no plans to reschedule or virtualize the event. This list of events was last updated on March 11. https://news.mit.edu/2020/creating-next-generation-data-scientists-peru-0309 IDSS and social impact group Aporta share a vision to educate and empower. Mon, 09 Mar 2020 13:35:01 -0400 https://news.mit.edu/2020/creating-next-generation-data-scientists-peru-0309 Scott Murray | Institute for Data, Systems, and Society “Participating in the MIT MicroMasters in Statistics and Data Science, I have discovered new concepts and skills that will allow me to become a data scientist,” says Karen Velasquez. “I am excited to apply what I have learned to challenges that will help NGOs in Peru.” When Velasquez graduated with a bachelor’s degree in statistical engineering from the Universidad Nacional de Ingeniería in Lima, Peru, she was among the top 10 percent of students in her class. Now, while working for a marketing and intelligence company in Peru, she’s expanding her education as one of the first 25 participants in the Aporta’s Advanced Program in Data Science and Global Skills, which supports a cohort of Peruvians through the MIT MicroMasters Program in Statistics and Data Science. Training future data scientists Both Aporta and the MIT Institute for Data, Systems, and Society (IDSS) recognize the urgent need to solve global challenges through rigorous and systemic analysis of large and complex datasets, using tools from statistics and computing. These approaches and techniques can bring new insights to societal challenges by detecting fake news, designing real-time demand response for the power grid, or maximizing the efficacy of vaccine intervention to prevent the spread of disease. This critical need led Aporta and IDSS to join forces to advance education in powerful data science methods and tools to train the next generation of data scientists in Peru. Aporta is leveraging the IDSS MicroMasters for a program of their own: the Advanced Program in Data Science and Global Skills. In partnership with IDSS faculty and staff, Aporta — a subsidiary of Peruvian conglomerate Breca Group — is offering the IDSS MicroMasters Program in Statistics and Data Science to a carefully vetted group of learners, along with additional content to develop skills in cross-cultural communication, teamwork, and leadership. The IDSS MicroMasters Program offers a rigorous MIT education, available from anywhere in the world. Through four online courses, learners in the MicroMasters program gain in-demand skills in data analysis and machine learning, plus get hands-on experience in applying these skills in challenges centered in economics and development. To support the Aporta cohort’s progress through the challenging courses of the MicroMasters program, IDSS recruits teaching assistants (TAs) with areas of expertise specific to each course. Learners interact with each other in physical space while receiving live instruction and feedback from TAs through online office hours. TAs use these sessions to identify challenge areas and develop individualized course materials. This personalized and interactive method creates a vibrant classroom experience for the learners, similar to being in a residential program on MIT’s campus. Custom TA-led sessions have “been beyond helpful to complement the online material,” said David Ascencios, a learner who is already working as a data scientist in Peru. The cohort has cleared the halfway mark of their journey through the program, and already the impact is significant. “I am very grateful to Aporta and to MIT,” says Johan Veramendi, a systems engineering graduate working in finance. “The program is an excellent opportunity to advance and guide my career into the world of data science.” Giving back Aporta’s educational outreach program began with a gift from Ana Maria Brescia Cafferata, the daughter of Grupo Breca’s late founder. It is a philanthropic endeavor with the goal of empowering Peruvian professionals with learning opportunities to enhance their careers, while providing much-needed talent across different industries and government. Data science is a young and growing field in South America, with untapped potential, an expanding job market, and increasing opportunity for both the private and public sectors. “This unique program has the vision to make Peru a hub in Latin America for analytics and artificial intelligence,” says Luis Herrera, who is balancing the program with his job as a software engineer and his role as a new father. “I share this vision and I think they are doing a great job. The MIT courses are very challenging and rewarding at the same time.” The pilot class of 25 learners represent a variety of socio-economic backgrounds. Most have college degrees. Thanks to Brescia Cafferata’s philanthropy, Aporta made a commitment to support all of them with scholarships throughout the program. Going forward, the initiative intends to become self-sustainable, granting as many scholarships as possible. “Her wish is to dedicate part of her parents’ legacy to the country she’s from, and to give back,” says Luz Fernandez Gandarias, director of the Institute for Advanced Analytics and Data Science within Aporta. “Her spirit is also behind the design of the program’s academic model, keeping people as the key point around which everything evolves, rather than technology. Ensuring the presence of an ethical conscience, recognizing the impact on people of technology — that humanistic view is something she’s always promoted.” For IDSS Director Munther Dahleh, the collaboration of Aporta and IDSS presents a compelling model of how MIT and IDSS can share their elite faculty and courses with the rest of the world: “IDSS wants to provide a rigorous data science education to the world. We think these skills are critical in the private sector, but also to solving global societal challenges.” This was the initial vision of Ana Maria Brescia Cafferata, who wants to give back to the country that gave her parents so much. Says Dahleh: “I am delighted to share the hopes and vision of Ana Maria. We have developed a unique program and partnership that aspires to educate students in an emerging field that is fundamentally changing the nature of work. In line with MIT’s mission of creating a better world, our goal is to create a more educated workforce capable of tackling the world’s challenges through enhanced data analysis and insights.” Learners in the Advanced Program in Data Science and Global Skills interact with each other in physical space while receiving live instruction and feedback from teaching assistants, recruited by the MIT Institute for Data, Systems, and Society, to support their journey through the MicroMasters Program in Statistics and Data Science. https://news.mit.edu/2020/creating-next-generation-data-scientists-peru-0309 IDSS and social impact group Aporta share a vision to educate and empower. Mon, 09 Mar 2020 13:35:01 -0400 https://news.mit.edu/2020/creating-next-generation-data-scientists-peru-0309 Scott Murray | Institute for Data, Systems, and Society “Participating in the MIT MicroMasters in Statistics and Data Science, I have discovered new concepts and skills that will allow me to become a data scientist,” says Karen Velasquez. “I am excited to apply what I have learned to challenges that will help NGOs in Peru.” When Velasquez graduated with a bachelor’s degree in statistical engineering from the Universidad Nacional de Ingeniería in Lima, Peru, she was among the top 10 percent of students in her class. Now, while working for a marketing and intelligence company in Peru, she’s expanding her education as one of the first 25 participants in the Aporta’s Advanced Program in Data Science and Global Skills, which supports a cohort of Peruvians through the MIT MicroMasters Program in Statistics and Data Science. Training future data scientists Both Aporta and the MIT Institute for Data, Systems, and Society (IDSS) recognize the urgent need to solve global challenges through rigorous and systemic analysis of large and complex datasets, using tools from statistics and computing. These approaches and techniques can bring new insights to societal challenges by detecting fake news, designing real-time demand response for the power grid, or maximizing the efficacy of vaccine intervention to prevent the spread of disease. This critical need led Aporta and IDSS to join forces to advance education in powerful data science methods and tools to train the next generation of data scientists in Peru. Aporta is leveraging the IDSS MicroMasters for a program of their own: the Advanced Program in Data Science and Global Skills. In partnership with IDSS faculty and staff, Aporta — a subsidiary of Peruvian conglomerate Breca Group — is offering the IDSS MicroMasters Program in Statistics and Data Science to a carefully vetted group of learners, along with additional content to develop skills in cross-cultural communication, teamwork, and leadership. The IDSS MicroMasters Program offers a rigorous MIT education, available from anywhere in the world. Through four online courses, learners in the MicroMasters program gain in-demand skills in data analysis and machine learning, plus get hands-on experience in applying these skills in challenges centered in economics and development. To support the Aporta cohort’s progress through the challenging courses of the MicroMasters program, IDSS recruits teaching assistants (TAs) with areas of expertise specific to each course. Learners interact with each other in physical space while receiving live instruction and feedback from TAs through online office hours. TAs use these sessions to identify challenge areas and develop individualized course materials. This personalized and interactive method creates a vibrant classroom experience for the learners, similar to being in a residential program on MIT’s campus. Custom TA-led sessions have “been beyond helpful to complement the online material,” said David Ascencios, a learner who is already working as a data scientist in Peru. The cohort has cleared the halfway mark of their journey through the program, and already the impact is significant. “I am very grateful to Aporta and to MIT,” says Johan Veramendi, a systems engineering graduate working in finance. “The program is an excellent opportunity to advance and guide my career into the world of data science.” Giving back Aporta’s educational outreach program began with a gift from Ana Maria Brescia Cafferata, the daughter of Grupo Breca’s late founder. It is a philanthropic endeavor with the goal of empowering Peruvian professionals with learning opportunities to enhance their careers, while providing much-needed talent across different industries and government. Data science is a young and growing field in South America, with untapped potential, an expanding job market, and increasing opportunity for both the private and public sectors. “This unique program has the vision to make Peru a hub in Latin America for analytics and artificial intelligence,” says Luis Herrera, who is balancing the program with his job as a software engineer and his role as a new father. “I share this vision and I think they are doing a great job. The MIT courses are very challenging and rewarding at the same time.” The pilot class of 25 learners represent a variety of socio-economic backgrounds. Most have college degrees. Thanks to Brescia Cafferata’s philanthropy, Aporta made a commitment to support all of them with scholarships throughout the program. Going forward, the initiative intends to become self-sustainable, granting as many scholarships as possible. “Her wish is to dedicate part of her parents’ legacy to the country she’s from, and to give back,” says Luz Fernandez Gandarias, director of the Institute for Advanced Analytics and Data Science within Aporta. “Her spirit is also behind the design of the program’s academic model, keeping people as the key point around which everything evolves, rather than technology. Ensuring the presence of an ethical conscience, recognizing the impact on people of technology — that humanistic view is something she’s always promoted.” For IDSS Director Munther Dahleh, the collaboration of Aporta and IDSS presents a compelling model of how MIT and IDSS can share their elite faculty and courses with the rest of the world: “IDSS wants to provide a rigorous data science education to the world. We think these skills are critical in the private sector, but also to solving global societal challenges.” This was the initial vision of Ana Maria Brescia Cafferata, who wants to give back to the country that gave her parents so much. Says Dahleh: “I am delighted to share the hopes and vision of Ana Maria. We have developed a unique program and partnership that aspires to educate students in an emerging field that is fundamentally changing the nature of work. In line with MIT’s mission of creating a better world, our goal is to create a more educated workforce capable of tackling the world’s challenges through enhanced data analysis and insights.” Learners in the Advanced Program in Data Science and Global Skills interact with each other in physical space while receiving live instruction and feedback from teaching assistants, recruited by the MIT Institute for Data, Systems, and Society, to support their journey through the MicroMasters Program in Statistics and Data Science. https://news.mit.edu/2020/creating-next-generation-data-scientists-peru-0309 IDSS and social impact group Aporta share a vision to educate and empower. Mon, 09 Mar 2020 13:35:01 -0400 https://news.mit.edu/2020/creating-next-generation-data-scientists-peru-0309 Scott Murray | Institute for Data, Systems, and Society “Participating in the MIT MicroMasters in Statistics and Data Science, I have discovered new concepts and skills that will allow me to become a data scientist,” says Karen Velasquez. “I am excited to apply what I have learned to challenges that will help NGOs in Peru.” When Velasquez graduated with a bachelor’s degree in statistical engineering from the Universidad Nacional de Ingeniería in Lima, Peru, she was among the top 10 percent of students in her class. Now, while working for a marketing and intelligence company in Peru, she’s expanding her education as one of the first 25 participants in the Aporta’s Advanced Program in Data Science and Global Skills, which supports a cohort of Peruvians through the MIT MicroMasters Program in Statistics and Data Science. Training future data scientists Both Aporta and the MIT Institute for Data, Systems, and Society (IDSS) recognize the urgent need to solve global challenges through rigorous and systemic analysis of large and complex datasets, using tools from statistics and computing. These approaches and techniques can bring new insights to societal challenges by detecting fake news, designing real-time demand response for the power grid, or maximizing the efficacy of vaccine intervention to prevent the spread of disease. This critical need led Aporta and IDSS to join forces to advance education in powerful data science methods and tools to train the next generation of data scientists in Peru. Aporta is leveraging the IDSS MicroMasters for a program of their own: the Advanced Program in Data Science and Global Skills. In partnership with IDSS faculty and staff, Aporta — a subsidiary of Peruvian conglomerate Breca Group — is offering the IDSS MicroMasters Program in Statistics and Data Science to a carefully vetted group of learners, along with additional content to develop skills in cross-cultural communication, teamwork, and leadership. The IDSS MicroMasters Program offers a rigorous MIT education, available from anywhere in the world. Through four online courses, learners in the MicroMasters program gain in-demand skills in data analysis and machine learning, plus get hands-on experience in applying these skills in challenges centered in economics and development. To support the Aporta cohort’s progress through the challenging courses of the MicroMasters program, IDSS recruits teaching assistants (TAs) with areas of expertise specific to each course. Learners interact with each other in physical space while receiving live instruction and feedback from TAs through online office hours. TAs use these sessions to identify challenge areas and develop individualized course materials. This personalized and interactive method creates a vibrant classroom experience for the learners, similar to being in a residential program on MIT’s campus. Custom TA-led sessions have “been beyond helpful to complement the online material,” said David Ascencios, a learner who is already working as a data scientist in Peru. The cohort has cleared the halfway mark of their journey through the program, and already the impact is significant. “I am very grateful to Aporta and to MIT,” says Johan Veramendi, a systems engineering graduate working in finance. “The program is an excellent opportunity to advance and guide my career into the world of data science.” Giving back Aporta’s educational outreach program began with a gift from Ana Maria Brescia Cafferata, the daughter of Grupo Breca’s late founder. It is a philanthropic endeavor with the goal of empowering Peruvian professionals with learning opportunities to enhance their careers, while providing much-needed talent across different industries and government. Data science is a young and growing field in South America, with untapped potential, an expanding job market, and increasing opportunity for both the private and public sectors. “This unique program has the vision to make Peru a hub in Latin America for analytics and artificial intelligence,” says Luis Herrera, who is balancing the program with his job as a software engineer and his role as a new father. “I share this vision and I think they are doing a great job. The MIT courses are very challenging and rewarding at the same time.” The pilot class of 25 learners represent a variety of socio-economic backgrounds. Most have college degrees. Thanks to Brescia Cafferata’s philanthropy, Aporta made a commitment to support all of them with scholarships throughout the program. Going forward, the initiative intends to become self-sustainable, granting as many scholarships as possible. “Her wish is to dedicate part of her parents’ legacy to the country she’s from, and to give back,” says Luz Fernandez Gandarias, director of the Institute for Advanced Analytics and Data Science within Aporta. “Her spirit is also behind the design of the program’s academic model, keeping people as the key point around which everything evolves, rather than technology. Ensuring the presence of an ethical conscience, recognizing the impact on people of technology — that humanistic view is something she’s always promoted.” For IDSS Director Munther Dahleh, the collaboration of Aporta and IDSS presents a compelling model of how MIT and IDSS can share their elite faculty and courses with the rest of the world: “IDSS wants to provide a rigorous data science education to the world. We think these skills are critical in the private sector, but also to solving global societal challenges.” This was the initial vision of Ana Maria Brescia Cafferata, who wants to give back to the country that gave her parents so much. Says Dahleh: “I am delighted to share the hopes and vision of Ana Maria. We have developed a unique program and partnership that aspires to educate students in an emerging field that is fundamentally changing the nature of work. In line with MIT’s mission of creating a better world, our goal is to create a more educated workforce capable of tackling the world’s challenges through enhanced data analysis and insights.” Learners in the Advanced Program in Data Science and Global Skills interact with each other in physical space while receiving live instruction and feedback from teaching assistants, recruited by the MIT Institute for Data, Systems, and Society, to support their journey through the MicroMasters Program in Statistics and Data Science. https://news.mit.edu/2020/data-feminism-catherine-dignazio-0309 Catherine D’Ignazio’s new book, “Data Feminism,” examines problems of bias and power that beset modern information. Mon, 09 Mar 2020 00:00:00 -0400 https://news.mit.edu/2020/data-feminism-catherine-dignazio-0309 Peter Dizikes | MIT News Office Suppose you would like to know mortality rates for women during childbirth, by country, around the world. Where would you look? One option is the WomanStats Project, the website of an academic research effort investigating the links between the security and activities of nation-states, and the security of the women who live in them.The project, founded in 2001, meets a need by patching together data from around the world. Many countries are indifferent to collecting statistics about women’s lives. But even where countries try harder to gather data, there are clear challenges to arriving at useful numbers — whether it comes to women’s physical security, property rights, and government participation, among many other issues.  For instance: In some countries, violations of women’s rights may be reported more regularly than in other places. That means a more responsive legal system may create the appearance of greater problems, when it provides relatively more support for women. The WomanStats Project notes many such complications.Thus the WomanStats Project offers some answers — for example, Australia, Canada, and much of Western Europe have low childbirth mortality rates — while also showing what the challenges are to taking numbers at face value. This, according to MIT professor Catherine D’Ignazio, makes the site unusual, and valuable.“The data never speak for themselves,” says D’Ignazio, referring to the general problem of finding reliable numbers about women’s lives. “There are always humans and institutions speaking for the data, and different people have their own agendas. The data are never innocent.”Now D’Ignazio, an assistant professor in MIT’s Department of Urban Studies and Planning, has taken a deeper look at this issue in a new book, co-authored with Lauren Klein, an associate professor of English and quantitative theory and methods at Emory University. In the book, “Data Feminism,” published this month by the MIT Press, the authors use the lens of intersectional feminism to scrutinize how data science reflects the social structures it emerges from.“Intersectional feminism examines unequal power,” write D’Ignazio and Klein, in the book’s introduction. “And in our contemporary world, data is power too. Because the power of data is wielded unjustly, it must be challenged and changed.”The 4 percent problemTo see a clear case of power relations generating biased data, D’Ignazio and Klein note, consider research led by MIT’s own Joy Buolamwini, who as a graduate student in a class studying facial-recognition programs, observed that the software in question could not “see” her face. Buolamwini found that for the facial-recognition system in question, the software was based on a set of faces which were 78 percent male and 84 percent white; only 4 percent were female and dark-skinned, like herself. Subsequent media coverage of Buolamwini’s work, D’Ignazio and Klein write, contained “a hint of shock.” But the results were probably less surprising to those who are not white males, they think.  “If the past is racist, oppressive, sexist, and biased, and that’s your training data, that is what you are tuning for,” D’Ignazio says.Or consider another example, from tech giant Amazon, which tested an automated system that used AI to sort through promising CVs sent in by job applicants. One problem: Because a high percentage of company employees were men, the algorithm favored men’s names, other things being equal. “They thought this would help [the] process, but of course what it does is train the AI [system] to be biased toward women, because they themselves have not hired that many women,” D’Ignazio observes.To Amazon’s credit, it did recognize the problem. Moreover, D’Ignazio notes, this kind of issue is a problem that can be addressed. “Some of the technologies can be reformed with a more participatory process, or better training data. … If we agree that’s a good goal, one path forward is to adjust your training set and include more people of color, more women.”“Who’s on the team? Who had the idea? Who’s benefiting?” Still, the question of who participates in data science is, as the authors write, “the elephant in the server room.” As of 2011, only 26 percent of all undergraduates receiving computer science degrees in the U.S. were women. That is not only a low figure, but actually a decline from past levels: In 1985, 37 percent of computer science graduates were women, the highest mark on record.As a result of the lack of diversity in the field, D’Ignazio and Klein believe, many data projects are radically limited in their ability to see all facets of the complex social situations they purport to measure. “We want to try to tune people in to these kinds of power relationships and why they matter deeply,” D’Ignazio says. “Who’s on the team? Who had the idea? Who’s benefiting from the project? Who’s potentially harmed by the project?”In all, D’Ignazio and Klein outline seven principles of data feminism, from examining and challenging power, to rethinking binary systems and hierarchies, and embracing pluralism. (Those statistics about gender and computer science graduates are limited, they note, by only using the “male” and “female” categories, thus excluding people who identify in different terms.)People interested in data feminism, the authors state, should also “value multiple forms of knowledge,” including firsthand knowledge that may lead us to question seemingly official data. Also, they should always consider the context in which data are generated, and “make labor visible” when it comes to data science. This last principle, the researchers note, speaks to the problem that even when women and other excluded people contribute to data projects, they often receive less credit for their work.For all the book’s critique of existing systems, programs, and practices, D’Ignazio and Klein are also careful to include examples of positive, successful efforts, such as the WomanStats project, which has grown and thrived over two decades.“For people who are data people but are new to feminism, we want to provide them with a very accessible introduction, and give them concepts and tools they can use in their practice,” D’Ignazio says. “We’re not imagining that people already have feminism in their toolkit. On the other hand, we are trying to speak to folks who are very tuned in to feminism or social justice principles, and highlight for them the ways data science is both problematic, but can be marshalled in the service of justice.” Catherine D’Ignazio is the co-author of a new book, “Data Feminism,” published by MIT Press in March 2020. Image: Diana Levine and MIT Press https://news.mit.edu/2020/protecting-sensitive-metadata-from-surveillance-0226 System ensures hackers eavesdropping on large networks can’t find out who’s communicating and when they’re doing so. Wed, 26 Feb 2020 00:00:00 -0500 https://news.mit.edu/2020/protecting-sensitive-metadata-from-surveillance-0226 Rob Matheson | MIT News Office MIT researchers have designed a scalable system that secures the metadata — such as who’s corresponding and when — of millions of users in communications networks, to help protect the information against possible state-level surveillance.Data encryption schemes that protect the content of online communications are prevalent today. Apps like WhatsApp, for instance, use “end-to-end encryption” (E2EE), a scheme that ensures third-party eavesdroppers can’t read messages sent by end users.But most of those schemes overlook metadata, which contains information about who’s talking, when the messages are sent, the size of message, and other information. Many times, that’s all a government or other hacker needs to know to track an individual. This can be especially dangerous for, say, a government whistleblower or people living in oppressive regimes talking with journalists.Systems that fully protect user metadata with cryptographic privacy are complex, and they suffer scalability and speed issues that have so far limited their practicality. Some methods can operate quickly but provide much weaker security. In a paper being presented at the USENIX Symposium on Networked Systems Design and Implementation, the MIT researchers describe “XRD” (for Crossroads), a metadata-protection scheme that can handle cryptographic communications from millions of users in minutes, whereas traditional methods with the same level of security would take hours to send everyone’s messages.“There is a huge lack in protection for metadata, which is sometimes very sensitive. The fact that I’m sending someone a message at all is not protected by encryption,” says first author Albert Kwon PhD ’19, a recent graduate from the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Encryption can protect content well. But how can we fully protect users from metadata leaks that a state-level adversary can leverage?”Joining Kwon on the paper are David Lu, an undergraduate in the Department of Electrical Engineering and Computer Science; and Srinivas Devadas, the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science in CSAIL.New spin on mix netsStarting in 2013, disclosures of classified information by Edward Snowden revealed widespread global surveillance by the U.S. government. Although the mass collection of metadata by the National Security Agency was subsequently discontinued, in 2014 former director of the NSA and the Central Intelligence Agency Michael Hayden explained that the government can often rely solely on metadata to find the information it’s seeking. As it happens, this is right around the time Kwon started his PhD studies.“That was like a punch to the cryptography and security communities,” Kwon says. “That meant encryption wasn’t really doing anything to stop spying in that regard.” Kwon spent most of his PhD program focusing on metadata privacy. With XRD, Kwon says he “put a new spin” on a traditional E2EE metadata-protecting scheme, called “mix nets,” which was invented decades ago but suffers from scalability issues.Mix nets use chains of servers, known as mixes, and public-private key encryption. The first server receives encrypted messages from many users and decrypts a single layer of encryption from each message. Then, it shuffles the messages in random order and transmits them to the next server, which does the same thing, and so on down the chain. The last server decrypts the final encryption layer and sends the message to the target receiver.Servers only know the identities of the immediate source (the previous server) and immediate destination (the next server). Basically, the shuffling and limited identity information breaks the link between source and destination users, making it very difficult for eavesdroppers to get that information. As long as one server in the chain is “honest”— meaning it follows protocol — metadata is almost always safe.However, “active attacks” can occur, in which a malicious server in a mix net tampers with the messages to reveal user sources and destinations. In short, the malicious server can drop messages or modify sending times to create communications patterns that reveal direct links between users.Some methods add cryptographic proofs between servers to ensure there’s been no tampering. These rely on public key cryptography, which is secure, but it’s also slow and limits scaling. For XRD, the researchers invented a far more efficient version of the cryptographic proofs, called “aggregate hybrid shuffle,” that guarantees servers are receiving and shuffling message correctly, to detect any malicious server activity.Each server has a secret private key and two shared public keys. Each server must know all the keys to decrypt and shuffle messages. Users encrypt messages in layers, using each server’s secret private key in its respective layer. When a server receives messages, it decrypts and shuffles them using one of the public keys combined with its own private key. Then, it uses the second public key to generate a proof confirming that it had, indeed, shuffled every message without dropping or manipulating any. All other servers in the chain use their secret private keys and the other servers’ public keys in a way that verifies this proof. If, at any point in the chain, a server doesn’t produce the proof or provides an incorrect proof, it’s immediately identified as malicious.This relies on a clever combination of the popular public key scheme with one called “authenticated encryption,” which uses only private keys but is very quick at generating and verifying the proofs. In this way, XRD achieves tight security from public key encryption while running quickly and efficiently.   To further boost efficiency, they split the servers into multiple chains and divide their use among users. (This is another traditional technique they improved upon.) Using some statistical techniques, they estimate how many servers in each chain could be malicious, based on IP addresses and other information. From that, they calculate how many servers need to be in each chain to guarantee there’s at least one honest server.  Then, they divide the users into groups that send duplicate messages to multiple, random chains, which further protects their privacy while speeding things up.Getting to real-timeIn computer simulations of activity from 2 million users sending messages on a network of 100 servers, XRD was able to get everyone’s messages through in about four minutes. Traditional systems using the same server and user numbers, and providing the same cryptographic security, took one to two hours.“This seems slow in terms of absolute speed in today’s communication world,” Kwon says. “But it’s important to keep in mind that the fastest systems right now [for metadata protection] take hours, whereas ours takes minutes.”Next, the researchers hope to make the network more robust to few users and in instances where servers go offline in the midst of operations, and to speed things up. “Four minutes is acceptable for sensitive messages and emails where two parties’ lives are in danger, but it’s not as natural as today’s internet,” Kwon says. “We want to get to the point where we’re sending metadata-protected messages in near real-time.” In a new metadata-protecting scheme, users send encrypted messages to multiple chains of servers, with each chain mathematically guaranteed to have at least one hacker-free server. Each server decrypts and shuffles the messages in random order, before shooting them to the next server in line. Image: courtesy of the researchers https://news.mit.edu/2020/mit-continues-advance-toward-greenhouse-gas-reduction-goals-0221 Investments in energy efficiency projects, sustainable design elements essential as campus transforms. Fri, 21 Feb 2020 14:20:01 -0500 https://news.mit.edu/2020/mit-continues-advance-toward-greenhouse-gas-reduction-goals-0221 Nicole Morell | Office of Sustainability At MIT, making a better world often starts on campus. That’s why, as the Institute works to find solutions to complex global problems, MIT has taken important steps to grow and transform its physical campus: adding new capacity, capabilities, and facilities to better support student life, education, and research. But growing and transforming the campus relies on resource and energy use — use that can exacerbate the complex global problem of climate change. This raises the question: How can an institution like MIT grow, and simultaneously work to lessen its greenhouse gas emissions and contributions to climate change?It’s a question — and a challenge — that MIT is committed to tackling.Tracking toward 2030 goals Guided by the 2015 Plan for Action on Climate Change, MIT continues to work toward a goal of a minimum of 32 percent reduction in campus greenhouse gas emissions by 2030. As reported in the MIT Office of Sustainability’s (MITOS) climate action plan update, campus greenhouse gas (GHG) emissions rose by 2 percent in 2019, in part due to a longer cooling season as well as the new MIT.nano facility coming fully online. Despite this, overall net emissions are 18 percent below the 2014 baseline, and MIT continues to track toward its 2030 goal.Joe Higgins, vice president for campus services and stewardship, is optimistic about MIT’s ability to not only meet, but exceed this current goal. “With this growth [to campus], we are discovering unparalleled opportunities to work toward carbon neutrality by collaborating with key stakeholders across the Institute, tapping into the creativity of our faculty, students, and researchers, and partnering with industry experts. We are committed to making steady progress toward achieving our GHG reduction goal,” he says.New growth to campus This past year marked the first full year of operation for the new MIT.nano facility. This facility includes many energy-intensive labs that necessitate high ventilation rates to meet the requirements of a nano technology clean room fabrication laboratory. As a result, the facility’s energy demands and GHG emissions can be much higher than a traditional science building. In addition, this facility — among others — uses specialty research gases that can act as potent greenhouse gases. Still, the 214,000-square-foot building has a number of sustainable, high-energy-efficiency design features, including an innovative air filtering process to support clean room standards while minimizing energy use. For these sustainable design elements, the facility was recognized with an International Institute for Sustainable Laboratories (I2SL) 2019 Go Beyond Award.In 2020, MIT.nano will be joined by new residential and multi-use buildings in both West Campus and Kendall Square, with the Vassar Street Residence and Kendall Square Sites 4 and 5 set to be completed. In keeping with MIT’s target for LEED v4 Gold Certification for new projects, these buildings were designed for high energy efficiency to minimize emissions and include a number of other sustainability measures, from green roofs to high-performance building envelopes. With new construction on campus, integrated design processes allow for sustainability and energy efficiency strategies to be adopted at the outset.Energy efficiency on an established campus For years, MIT has been keenly focused on increasing the energy efficiency and reducing emissions of its existing buildings, but as the campus grows, reducing emissions of current buildings through deep energy enhancements is an increasingly important part of offsetting emissions from new growth.To best accomplish this, the Department of Facilities — in close collaboration with the Office of Sustainability — has developed and rolled out a governance structure that relies on cross-functional teams to create new standards and policies, identify opportunities, develop projects, and assess progress relevant to building efficiency and emissions reduction. “Engaging across campus and across departments is essential to building out MIT’s full capacity to advance emissions reductions,” explains Director of Sustainability Julie Newman.These cross-functional teams — which include Campus Construction; Campus Services and Maintenance; Environment, Health, and Safety; Facilities Engineering; the Office of Sustainability; and Utilities — have focused on a number of strategies in the past year, including both building-wide and targeted energy strategies that have revealed priority candidates for energy retrofits to drive efficiency and minimize emissions.Carlo Fanone, director of facilities engineering, explains that “the cross-functional teams play an especially critical role at MIT, since we are a district energy campus. We supply most of our own energy, we distribute it, and we are the end users, so the teams represent a holistic approach that looks at all three of these elements equally — supply, distribution, and end-use — and considers energy solutions that address any or all of these elements.” Fanone notes that MIT has also identified 25 facilities on campus that have a high energy-use intensity and a high greenhouse gas emissions footprint. These 25 buildings account for up to 50 percent of energy consumption on the MIT campus. “Going forward,” Fanone says, “we are focusing our energy work on these buildings and on other energy enhancements that could have a measurable impact on the progress toward MIT’s 2030 goal.”Armed with these data, the Department of Facilities last year led retrofits for smart lighting and mechanical systems upgrades, as well as smart building management systems, in a number of buildings across campus. These building audits will continue to guide future projects focused on improving and optimizing energy elements such as heat recovery, lighting, and building systems controls.In addition to building-level efficiency improvements, MIT’s Central Utilities Plant upgrade is expected to contribute significantly to the reduction of on-campus emissions in upcoming years. The upgraded plant — set to be completed this year — will incorporate more efficient equipment and state-of-the-art controls. Between this upgrade, a fuel switch improvement made in 2015, and the building-level energy improvements, regulated pollutant emissions on campus are expected to reduce by more than 25 percent and campus greenhouse gas emissions by 10 percent from 2014 levels, helping to offset a projected 10 percent increase in greenhouse gas emissions due to energy demands created by new growth.Climate research and action on campus As MIT explores energy efficiency opportunities, the campus itself plays an important role as an incubator for new ideas.In 2019, MITOS director Julie Newman and professor of mechanical engineering Timothy Gutowski are once again teaching 11.S938 / 2.S999 (Solving for Carbon Neutrality at MIT) this semester. “The course, along with others that have emerged across campus, provides students the opportunity to devise ideas and solutions for real-world challenges while connecting them back to campus. It also gives the students a sense of ownership on this campus, sharing ideas to chart the course for carbon-neutral MIT,” Newman says.Also on campus, a new energy storage project is being developed to test the feasibility and scalability of using different battery storage technologies to redistribute electricity provided by variable renewable energy. Funded by a Campus Sustainability Incubator Fund grant and led by Jessika Trancik, associate professor in the Institute for Data, Systems, and Society, the project aims to test software approaches to synchronizing energy demand and supply and evaluate the performance of different energy-storage technologies against these use cases. It has the benefit of connecting on-campus climate research with climate action. “Building this storage testbed, and testing technologies under real-world conditions, can inform new algorithms and battery technologies and act as a multiplier, so that the lessons we learn at MIT can be applied far beyond campus,” says Trancik of the project.Supporting on-campus efforts MIT’s work toward emissions reductions already extends beyond campus as the Institute continues to benefit from the Institute’s 25-year commitment to purchase electricity generated through its Summit Farms Power Purchase Agreement (PPA), which enabled the construction of a 650-acre, 60-megawatt solar farm in North Carolina. Through the purchase of 87,300 megawatt-hours of solar power, MIT was able to offset over 30,000 metric tons of greenhouse gas emissions from our on-campus operations in 2019.The Summit Farms PPA model has provided inspiration for similar projects around the country and has also demonstrated what MIT can accomplish through partnership. MIT continues to explore the possibility of collaborating on similar large power-purchase agreements, possibly involving other local institutions and city governments.Looking ahead As the campus continues to work toward reducing emissions, Fanone notes that a comprehensive approach will help MIT address the challenge of growing a campus while reducing emissions. “District-level energy solutions, additional renewables, coupled with energy enhancements within our buildings, will allow MIT to offset growth and meet our 2030 GHG goals,” says Fanone. Adds Newman, “It’s an exciting time that MIT is now positioned to put the steps in place to respond to this global crisis at the local level.” How can an institution like MIT grow, and simultaneously work to lessen its greenhouse gas emissions and contributions to climate change? Photo: Maia Weinstock https://news.mit.edu/2020/patternex-machine-learning-cybersecurity-0221 PatternEx merges human and machine expertise to spot and respond to hacks. Fri, 21 Feb 2020 14:12:18 -0500 https://news.mit.edu/2020/patternex-machine-learning-cybersecurity-0221 Zach Winn | MIT News Office Being a cybersecurity analyst at a large company today is a bit like looking for a needle in a haystack — if that haystack were hurtling toward you at fiber optic speed.Every day, employees and customers generate loads of data that establish a normal set of behaviors. An attacker will also generate data while using any number of techniques to infiltrate the system; the goal is to find that “needle” and stop it before it does any damage.The data-heavy nature of that task lends itself well to the number-crunching prowess of machine learning, and an influx of AI-powered systems have indeed flooded the cybersecurity market over the years. But such systems can come with their own problems, namely a never-ending stream of false positives that can make them more of a time suck than a time saver for security analysts.MIT startup PatternEx starts with the assumption that algorithms can’t protect a system on their own. The company has developed a closed loop approach whereby machine-learning models flag possible attacks and human experts provide feedback. The feedback is then incorporated into the models, improving their ability to flag only the activity analysts care about in the future.“Most machine learning systems in cybersecurity have been doing anomaly detection,” says Kalyan Veeramachaneni, a co-founder of PatternEx and a principal research scientist at MIT. “The problem with that, first, is you need a baseline [of normal activity]. Also, the model is usually unsupervised, so it ends up showing a lot of alerts, and people end up shutting it down. The big difference is that PatternEx allows the analyst to inform the system and then it uses that feedback to filter out false positives.”The result is an increase in analyst productivity. When compared to a generic anomaly detection software program, PatternEx’s Virtual Analyst Platform successfully identified 10 times more threats through the same number of daily alerts, and its advantage persisted even when the generic system gave analysts five times more alerts per day.First deployed in 2016, today the company’s system is being used by security analysts at large companies in a variety of industries along with firms that offer cybersecurity as a service.Merging human and machine approaches to cybersecurityVeeramachaneni came to MIT in 2009 as a postdoc and now directs a research group in the Laboratory for Information and Decision Systems. His work at MIT primarily deals with big data science and machine learning, but he didn’t think deeply about applying those tools to cybersecurity until a brainstorming session with PatternEx co-founders Costas Bassias, Uday Veeramachaneni, and Vamsi Korrapati in 2013.Ignacio Arnaldo, who worked with Veeramachaneni as a postdoc at MIT between 2013 and 2015, joined the company shortly after. Veeramachaneni and Arnaldo knew from their time building tools for machine-learning researchers at MIT that a successful solution would need to seamlessly integrate machine learning with human expertise.“A lot of the problems people have with machine learning arise because the machine has to work side by side with the analyst,” Veeramachaneni says, noting that detected attacks still must be presented to humans in an understandable way for further investigation. “It can’t do everything by itself. Most systems, even for something as simple as giving out a loan, is augmentation, not machine learning just taking decisions away from humans.”The company’s first partnership was with a large online retailer, which allowed the founders to train their models to identify potentially malicious behavior using real-world data. One by one, they trained their algorithms to flag different types of attacks using sources like Wi-Fi access logs, authentication logs, and other user behavior in the network.The early models worked best in retail, but Veeramachaneni knew how much businesses in other industries were struggling to apply machine learning in their operations from his many conversations with company executives at MIT (a subject PatternEx recently published a paper on).“MIT has done an incredible job since I got here 10 years ago bringing industry through the doors,” Veeramachaneni says. He estimates that in the past six years as a member of MIT’s Industrial Liaison Program he’s had 200 meetings with members of the private sector to talk about the problems they’re facing. He has also used those conversations to make sure his lab’s research is addressing relevant problems.In addition to enterprise customers, the company began offering its platform to security service providers and teams that specialize in hunting for undetected cyberattacks in networks.Today analysts can build machine learning models through PatternEx’s platform without writing a line of code, lowering the bar for people to use machine learning as part of a larger trend in the industry toward what Veeramachaneni calls the democratization of AI.“There’s not enough time in cybersecurity; it can’t take hours or even days to understand why an attack is happening,” Veeramachaneni says. “That’s why getting the analyst the ability to build and tweak machine learning models  is the most critical aspect of our system.”Giving security analysts an armyPatternEx’s Virtual Analyst Platform is designed to make security analysts feel like they have an army of assistants combing through data logs and presenting them with the most suspicious behavior on their network.The platform uses machine learning models to go through more than 50 streams of data and identify suspicious behavior. It then presents that information to the analyst for feedback, along with charts and other data visualizations that help the analyst decide how to proceed. After the analyst determines whether or not the behavior is an attack, that feedback is incorporated back into the models, which are updated across PatternEx’s entire customer base.“Before machine learning, someone would catch an attack, probably a little late, they might name it, and then they’ll announce it, and all the other companies will call and find out about it and go in and check their data,” Veeramachaneni says. “For us, if there’s an attack, we take that data, and because we have multiple customers, we have to transfer that in real time to other customer’s data to see if it’s happening with them too. We do that very efficiently on a daily basis.”The moment the system is up and running with new customers, it is able to identify 40 different types of cyberattacks using 170 different prepackaged machine learning models. Arnaldo notes that as the company works to grow those figures, customers are also adding to PatternEx’s model base by building solutions on the platform that address specific threats they’re facing.Even if customers aren’t building their own models on the platform, they can deploy PatternEx’s system out of the box, without any machine learning expertise, and watch it get smarter automatically.By providing that flexibility, PatternEx is bringing the latest tools in artificial intelligence to the people who understand their industries most intimately. It all goes back to the company’s founding principle of empowering humans with artificial intelligence instead of replacing them.“The target users of the system are not skilled data scientists or machine learning experts — profiles that are hard for cybersecurity teams to hire — but rather domain experts already on their payroll that have the deepest understanding of their data and uses cases,” Arnaldo says. PatternEx’s Virtual Analyst Platform uses machine learning models to detect suspicious activity on a network. That activity is then presented to human analysts for feedback that improves the systems’ ability to flag activity analysts care about. https://news.mit.edu/2020/automated-rewrite-wikipedia-articles-0212 Text-generating tool pinpoints and replaces specific information in sentences while retaining humanlike grammar and style. Wed, 12 Feb 2020 13:51:56 -0500 https://news.mit.edu/2020/automated-rewrite-wikipedia-articles-0212 Rob Matheson | MIT News Office A system created by MIT researchers could be used to automatically update factual inconsistencies in Wikipedia articles, reducing time and effort spent by human editors who now do the task manually.Wikipedia comprises millions of articles that are in constant need of edits to reflect new information. That can involve article expansions, major rewrites, or more routine modifications such as updating numbers, dates, names, and locations. Currently, humans across the globe volunteer their time to make these edits.  In a paper being presented at the AAAI Conference on Artificial Intelligence, the researchers describe a text-generating system that pinpoints and replaces specific information in relevant Wikipedia sentences, while keeping the language similar to how humans write and edit.The idea is that humans would type into an interface an unstructured sentence with updated information, without needing to worry about style or grammar. The system would then search Wikipedia, locate the appropriate page and outdated sentence, and rewrite it in a humanlike fashion. In the future, the researchers say, there’s potential to build a fully automated system that identifies and uses the latest information from around the web to produce rewritten sentences in corresponding Wikipedia articles that reflect updated information.“There are so many updates constantly needed to Wikipedia articles. It would be beneficial to automatically modify exact portions of the articles, with little to no human intervention,” says Darsh Shah, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and one of the lead authors. “Instead of hundreds of people working on modifying each Wikipedia article, then you’ll only need a few, because the model is helping or doing it automatically. That offers dramatic improvements in efficiency.”Many other bots exist that make automatic Wikipedia edits. Typically, those work on mitigating vandalism or dropping some narrowly defined information into predefined templates, Shah says. The researchers’ model, he says, solves a harder artificial intelligence problem: Given a new piece of unstructured information, the model automatically modifies the sentence in a humanlike fashion. “The other [bot] tasks are more rule-based, while this is a task requiring reasoning over contradictory parts in two sentences and generating a coherent piece of text,” he says.The system can be used for other text-generating applications as well, says co-lead author and CSAIL graduate student Tal Schuster. In their paper, the researchers also used it to automatically synthesize sentences in a popular fact-checking dataset that helped reduce bias, without manually collecting additional data. “This way, the performance improves for automatic fact-verification models that train on the dataset for, say, fake news detection,” Schuster says.Shah and Schuster worked on the paper with their academic advisor Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science and a professor in CSAIL.Neutrality masking and fusingBehind the system is a fair bit of text-generating ingenuity in identifying contradictory information between, and then fusing together, two separate sentences. It takes as input an “outdated” sentence from a Wikipedia article, plus a separate “claim” sentence that contains the updated and conflicting information. The system must automatically delete and keep specific words in the outdated sentence, based on information in the claim, to update facts but maintain style and grammar. That’s an easy task for humans, but a novel one in machine learning.For example, say there’s a required update to this sentence (in bold): “Fund A considers 28 of their 42 minority stakeholdings in operationally active companies to be of particular significance to the group.” The claim sentence with updated information may read: “Fund A considers 23 of 43 minority stakeholdings significant.” The system would locate the relevant Wikipedia text for “Fund A,” based on the claim. It then automatically strips out the outdated numbers (28 and 42) and replaces them with the new numbers (23 and 43), while keeping the sentence exactly the same and grammatically correct. (In their work, the researchers ran the system on a dataset of specific Wikipedia sentences, not on all Wikipedia pages.)The system was trained on a popular dataset that contains pairs of sentences, in which one sentence is a claim and the other is a relevant Wikipedia sentence. Each pair is labeled in one of three ways: “agree,” meaning the sentences contain matching factual information; “disagree,” meaning they contain contradictory information; or “neutral,” where there’s not enough information for either label. The system must make all disagreeing pairs agree, by modifying the outdated sentence to match the claim. That requires using two separate models to produce the desired output.The first model is a fact-checking classifier — pretrained to label each sentence pair as “agree,” “disagree,” or “neutral” — that focuses on disagreeing pairs. Running in conjunction with the classifier is a custom “neutrality masker” module that identifies which words in the outdated sentence contradict the claim. The module removes the minimal number of words required to “maximize neutrality” — meaning the pair can be labeled as neutral. That’s the starting point: While the sentences don’t agree, they no longer contain obviously contradictory information. The module creates a binary “mask” over the outdated sentence, where a 0 gets placed over words that most likely require deleting, while a 1 goes on top of keepers.After masking, a novel two-encoder-decoder framework is used to generate the final output sentence. This model learns compressed representations of the claim and the outdated sentence. Working in conjunction, the two encoder-decoders fuse the dissimilar words from the claim, by sliding them into the spots left vacant by the deleted words (the ones covered with 0s) in the outdated sentence.In one test, the model scored higher than all traditional methods, using a technique called “SARI” that measures how well machines delete, add, and keep words compared to the way humans modify sentences. They used a dataset with manually edited Wikipedia sentences, which the model hadn’t seen before. Compared to several traditional text-generating methods, the new model was more accurate in making factual updates and its output more closely resembled human writing. In another test, crowdsourced humans scored the model (on a scale of 1 to 5) based on how well its output sentences contained factual updates and matched human grammar. The model achieved average scores of 4 in factual updates and 3.85 in matching grammar.Removing biasThe study also showed that the system can be used to augment datasets to eliminate bias when training detectors of “fake news,” a form of propaganda containing disinformation created to mislead readers in order to generate website views or steer public opinion. Some of these detectors train on datasets of agree-disagree sentence pairs to “learn” to verify a claim by matching it to given evidence.In these pairs, the claim will either match certain information with a supporting “evidence” sentence from Wikipedia (agree) or it will be modified by humans to include information contradictory to the evidence sentence (disagree). The models are trained to flag claims with refuting evidence as “false,” which can be used to help identify fake news.Unfortunately, such datasets currently come with unintended biases, Shah says: “During training, models use some language of the human written claims as “give-away” phrases to mark them as false, without relying much on the corresponding evidence sentence. This reduces the model’s accuracy when evaluating real-world examples, as it does not perform fact-checking.”The researchers used the same deletion and fusion techniques from their Wikipedia project to balance the disagree-agree pairs in the dataset and help mitigate the bias. For some “disagree” pairs, they used the modified sentence’s false information to regenerate a fake “evidence” supporting sentence. Some of the give-away phrases then exist in both the “agree” and “disagree” sentences, which forces models to analyze more features. Using their augmented dataset, the researchers reduced the error rate of a popular fake-news detector by 13 percent.“If you have a bias in your dataset, and you’re fooling your model into just looking at one sentence in a disagree pair to make predictions, your model will not survive the real world,” Shah says. “We make models look at both sentences in all agree-disagree pairs.” MIT researchers have created an automated text-generating system that pinpoints and replaces specific information in relevant Wikipedia sentences, while keeping the language similar to how humans write and edit. Image: Christine Daniloff, MIT https://news.mit.edu/2020/brainstorming-energy-saving-hacks-satori-mit-supercomputer-0211 Three-day hackathon explores methods for making artificial intelligence faster and more sustainable. Tue, 11 Feb 2020 11:50:01 -0500 https://news.mit.edu/2020/brainstorming-energy-saving-hacks-satori-mit-supercomputer-0211 Kim Martineau | MIT Quest for Intelligence Mohammad Haft-Javaherian planned to spend an hour at the Green AI Hackathon — just long enough to get acquainted with MIT’s new supercomputer, Satori. Three days later, he walked away with $1,000 for his winning strategy to shrink the carbon footprint of artificial intelligence models trained to detect heart disease.  “I never thought about the kilowatt-hours I was using,” he says. “But this hackathon gave me a chance to look at my carbon footprint and find ways to trade a small amount of model accuracy for big energy savings.”  Haft-Javaherian was among six teams to earn prizes at a hackathon co-sponsored by the MIT Research Computing Project and MIT-IBM Watson AI Lab Jan. 28-30. The event was meant to familiarize students with Satori, the computing cluster IBM donated to MIT last year, and to inspire new techniques for building energy-efficient AI models that put less planet-warming carbon dioxide into the air.  The event was also a celebration of Satori’s green-computing credentials. With an architecture designed to minimize the transfer of data, among other energy-saving features, Satori recently earned fourth place on the Green500 list of supercomputers. Its location gives it additional credibility: It sits on a remediated brownfield site in Holyoke, Massachusetts, now the Massachusetts Green High Performance Computing Center, which runs largely on low-carbon hydro, wind and nuclear power. A postdoc at MIT and Harvard Medical School, Haft-Javaherian came to the hackathon to learn more about Satori. He stayed for the challenge of trying to cut the energy intensity of his own work, focused on developing AI methods to screen the coronary arteries for disease. A new imaging method, optical coherence tomography, has given cardiologists a new tool for visualizing defects in the artery walls that can slow the flow of oxygenated blood to the heart. But even the experts can miss subtle patterns that computers excel at detecting. At the hackathon, Haft-Javaherian ran a test on his model and saw that he could cut its energy use eight-fold by reducing the time Satori’s graphics processors sat idle. He also experimented with adjusting the model’s number of layers and features, trading varying degrees of accuracy for lower energy use.  A second team, Alex Andonian and Camilo Fosco, also won $1,000 by showing they could train a classification model nearly 10 times faster by optimizing their code and losing a small bit of accuracy. Graduate students in the Department of Electrical Engineering and Computer Science (EECS), Andonian and Fosco are currently training a classifier to tell legitimate videos from AI-manipulated fakes, to compete in Facebook’s Deepfake Detection Challenge. Facebook launched the contest last fall to crowdsource ideas for stopping the spread of misinformation on its platform ahead of the 2020 presidential election. If a technical solution to deepfakes is found, it will need to run on millions of machines at once, says Andonian. That makes energy efficiency key. “Every optimization we can find to train and run more efficient models will make a huge difference,” he says. To speed up the training process, they tried streamlining their code and lowering the resolution of their 100,000-video training set by eliminating some frames. They didn’t expect a solution in three days, but Satori’s size worked in their favor. “We were able to run 10 to 20 experiments at a time, which let us iterate on potential ideas and get results quickly,” says Andonian.  As AI continues to improve at tasks like reading medical scans and interpreting video, models have grown bigger and more calculation-intensive, and thus, energy intensive. By one estimate, training a large language-processing model produces nearly as much carbon dioxide as the cradle-to-grave emissions from five American cars. The footprint of the typical model is modest by comparison, but as AI applications proliferate its environmental impact is growing.  One way to green AI, and tame the exponential growth in demand for training AI, is to build smaller models. That’s the approach that a third hackathon competitor, EECS graduate student Jonathan Frankle, took. Frankle is looking for signals early in the training process that point to subnetworks within the larger, fully-trained network that can do the same job. The idea builds on his award-winning Lottery Ticket Hypothesis paper from last year that found a neural network could perform with 90 percent fewer connections if the right subnetwork was found early in training. The hackathon competitors were judged by John Cohn, chief scientist at the MIT-IBM Watson AI Lab, Christopher Hill, director of MIT’s Research Computing Project, and Lauren Milechin, a research software engineer at MIT.  The judges recognized four other teams: Department of Earth, Atmospheric and Planetary Sciences (EAPS) graduate students Ali Ramadhan, Suyash Bire, and James Schloss, for adapting the programming language Julia for Satori; MIT Lincoln Laboratory postdoc Andrew Kirby, for adapting code he wrote as a graduate student to Satori using a library designed for easy programming of computing architectures; and Department of Brain and Cognitive Sciences graduate students Jenelle Feather and Kelsey Allen, for applying a technique that drastically simplifies models by cutting their number of parameters. IBM developers were on hand to answer questions and gather feedback.  “We pushed the system — in a good way,” says Cohn. “In the end, we improved the machine, the documentation, and the tools around it.”  Going forward, Satori will be joined in Holyoke by TX-Gaia, Lincoln Laboratory’s new supercomputer. Together, they will provide feedback on the energy use of their workloads. “We want to raise awareness and encourage users to find innovative ways to green-up all of their computing,” says Hill.  Several dozen students participated in the Green AI Hackathon, co-sponsored by the MIT Research Computing Project and MIT-IBM Watson AI Lab. Photo panel: Samantha Smiley https://news.mit.edu/2020/hey-alexa-sorry-i-fooled-you-0207 MIT’s new system TextFooler can trick the types of natural-language-processing systems that Google uses to help power its search results, including audio for Google Home. Fri, 07 Feb 2020 11:20:01 -0500 https://news.mit.edu/2020/hey-alexa-sorry-i-fooled-you-0207 Rachel Gordon | MIT CSAIL A human can likely tell the difference between a turtle and a rifle. Two years ago, Google’s AI wasn’t so sure. For quite some time, a subset of computer science research has been dedicated to better understanding how machine-learning models handle these “adversarial” attacks, which are inputs deliberately created to trick or fool machine-learning algorithms.  While much of this work has focused on speech and images, recently, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) tested the boundaries of text. They came up with “TextFooler,” a general framework that can successfully attack natural language processing (NLP) systems — the types of systems that let us interact with our Siri and Alexa voice assistants — and “fool” them into making the wrong predictions.  One could imagine using TextFooler for many applications related to internet safety, such as email spam filtering, hate speech flagging, or “sensitive” political speech text detection — which are all based on text classification models.  “If those tools are vulnerable to purposeful adversarial attacking, then the consequences may be disastrous,” says Di Jin, MIT PhD student and lead author on a new paper about TextFooler. “These tools need to have effective defense approaches to protect themselves, and in order to make such a safe defense system, we need to first examine the adversarial methods.”  TextFooler works in two parts: altering a given text, and then using that text to test two different language tasks to see if the system can successfully trick machine-learning models.   The system first identifies the most important words that will influence the target model’s prediction, and then selects the synonyms that fit contextually. This is all while maintaining grammar and the original meaning to look “human” enough, until the prediction is altered.  Then, the framework is applied to two different tasks — text classification, and entailment (which is the relationship between text fragments in a sentence), with the goal of changing the classification or invalidating the entailment judgment of the original models.  In one example, TextFooler’s input and output were: “The characters, cast in impossibly contrived situations, are totally estranged from reality.”  “The characters, cast in impossibly engineered circumstances, are fully estranged from reality.” In this case, when testing on an NLP model, it gets the example input right, but then gets the modified input wrong.  In total, TextFooler successfully attacked three target models, including “BERT,” the popular open-source NLP model. It fooled the target models with an accuracy of over 90 percent to under 20 percent, by changing only 10 percent of the words in a given text. The team evaluated success on three criteria: changing the model’s prediction for classification or entailment; whether it looked similar in meaning to a human reader, compared with the original example; and whether the text looked natural enough.  The researchers note that while attacking existing models is not the end goal, they hope that this work will help more abstract models generalize to new, unseen data.  “The system can be used or extended to attack any classification-based NLP models to test their robustness,” says Jin. “On the other hand, the generated adversaries can be used to improve the robustness and generalization of deep-learning models via adversarial training, which is a critical direction of this work.”  Jin wrote the paper alongside MIT Professor Peter Szolovits, Zhijing Jin of the University of Hong Kong, and Joey Tianyi Zhou of A*STAR, Singapore. They will present the paper at the AAAI Conference on Artificial Intelligence in New York.  CSAIL PhD student Di Jin led the development of the TextFooler system. Photo: Jason Dorfman/MIT CSAIL https://news.mit.edu/2020/college-for-the-computing-age-0204 With the initial organizational structure in place, the MIT Schwarzman College of Computing moves forward with implementation. Tue, 04 Feb 2020 12:30:01 -0500 https://news.mit.edu/2020/college-for-the-computing-age-0204 Terri Park | MIT Schwarzman College of Computing The mission of the MIT Stephen A. Schwarzman College of Computing is to address the opportunities and challenges of the computing age — from hardware to software to algorithms to artificial intelligence (AI) — by transforming the capabilities of academia in three key areas: supporting the rapid evolution and growth of computer science and AI; facilitating collaborations between computing and other disciplines; and focusing on social and ethical responsibilities of computing through combining technological approaches and insights from social science and humanities, and through engagement beyond academia. Since starting his position in August 2019, Daniel Huttenlocher, the inaugural dean of the MIT Schwarzman College of Computing, has been working with many stakeholders in designing the initial organizational structure of the college. Beginning with the College of Computing Task Force Working Group reports and feedback from the MIT community, the structure has been developed through an iterative process of draft plans yielding a 26-page document outlining the initial academic organization of the college that is designed to facilitate the college mission through improved coordination and evolution of existing computing programs at MIT, improved collaboration in computing across disciplines, and development of new cross-cutting activities and programs, notably in the social and ethical responsibilities of computing. “The MIT Schwarzman College of Computing is both bringing together existing MIT programs in computing and developing much-needed new cross-cutting educational and research programs,” says Huttenlocher. “For existing programs, the college helps facilitate coordination and manage the growth in areas such as computer science, artificial intelligence, data systems and society, and operations research, as well as helping strengthen interdisciplinary computing programs such as computational science and engineering. For new areas, the college is creating cross-cutting platforms for the study and practice of social and ethical responsibilities of computing, for multi-departmental computing education, and for incubating new interdisciplinary computing activities.” The following existing departments, institutes, labs, and centers are now part of the college: Department of Electrical Engineering and Computer (EECS), which has been reorganized into three overlapping sub-units of electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D), and is jointly part of the MIT Schwarzman College of Computing and School of Engineering; Operations Research Center (ORC), which is jointly part of the MIT Schwarzman College of Computing and MIT Sloan School of Management; Institute for Data, Systems, and Society (IDSS), which will be increasing its focus on the societal aspects of its mission while also continuing to support statistics across MIT, and including the Technology and Policy Program (TPP) and Sociotechnical Systems Research Center (SSRC); Center for Computational Science Engineering (CCSE), which is being renamed from the Center for Computational Engineering and broadening its focus in the sciences; Computer Science and Artificial Intelligence Laboratory (CSAIL); Laboratory for Information and Decision Systems (LIDS); and Quest for Intelligence. With the initial structure in place, Huttenlocher, the college leadership team, and the leaders of the academic units that are part of the college, in collaboration with departments in all five schools, are actively moving forward with curricular and programmatic development, including the launch of two new areas, the Common Ground for Computing Education and the Social and Ethical Responsibilities of Computing (SERC). Still in the early planning stages, these programs are the aspects of the college that are designed to cut across lines and involve a number of departments throughout MIT. Other programs are expected to be introduced as the college continues to take shape. “The college is an Institute-wide entity, working with and across all five schools,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science, who was part of the task force steering committee. “Its continued growth and focus depend greatly on the input of our MIT community, a process which began over a year ago. I’m delighted that Dean Huttenlocher and the college leadership team have engaged the community for collaboration and discussion around the plans for the college.” With these organizational changes, students, faculty, and staff in these units are members of the college, and in some cases, jointly with a school, as will be those who are engaged in the new cross-cutting activities in SERC and Common Ground. “A question we get frequently,” says Huttenlocher, “is how to apply to the college. As is the case throughout MIT, undergraduate admissions are handled centrally, and graduate admissions are handled by each individual department or graduate program.” Advancing computing Despite the unprecedented growth in computing, there remains substantial unmet demand for expertise. In academia, colleges and universities worldwide are faced with oversubscribed programs in computer science and the constant need to keep up with rapidly changing materials at both the graduate and undergraduate level. According to Huttenlocher, the computing fields are evolving at a pace today that is beyond the capabilities of current academic structures to handle. “As academics, we pride ourselves on being generators of new knowledge, but academic institutions themselves don’t change that quickly. The rise of AI is probably the biggest recent example of that, along with the fact that about 40 percent of MIT undergraduates are majoring in computer science, where we have 7 percent of the MIT faculty.” In order to help meet this demand, MIT is increasing its academic capacity in computing and AI with 50 new faculty positions — 25 will be core computing positions in CS, AI, and related areas, and 25 will be shared jointly with departments. Searches are now active to recruit core faculty in CS and AI+D, and for joint faculty with MIT Philosophy, the Department of Brain and Cognitive Sciences, and several interdisciplinary institutes. The new shared faculty searches will largely be conducted around the concept of “clusters” to build capacity at MIT in important computing areas that cut across disciplines, departments, and schools. Huttenlocher, the provost, and the five school deans will work to identify themes based on input from departments so that recruiting can be undertaken during the next academic year. Cross-cutting collaborations in computing Building on the history of strong faculty participation in interdepartmental labs, centers, and initiatives, the MIT Schwarzman College of Computing provides several forms of membership in the college based on cross-cutting research, teaching, or external engagement activities. While computing is affecting intellectual inquiry in almost every discipline, Huttenlocher is quick to stress that “it’s bi-directional.” He notes that existing collaborations across various schools and departments, such as MIT Digital Humanities, as well as opportunities for new such collaborations, are key to the college mission because in the same way that “computing is changing thinking in the disciplines; the disciplines are changing the way people do computing.” Under the leadership of Asu Ozdaglar, the deputy dean of academics and department head of EECS, the college is developing the Common Ground for Computing Education, an interdepartmental teaching collaborative that will facilitate the offering of computing classes and coordination of computing-related curricula across academic units. The objectives of this collaborative are to provide opportunities for faculty across departments to work together, including co-teaching classes, creating new undergraduate majors or minors such as in AI+D, as well as facilitating undergraduate blended degrees such as 6-14 (Computer Science, Economics, and Data Science), 6-9 (Computation and Cognition), 11-6 (Urban Science and Planning with Computer Science), 18-C (Mathematics with Computer Science), and others. “It is exciting to bring together different areas of computing with methodological and substantive commonalities as well as differences around one table,” says Ozdaglar. “MIT faculty want to collaborate in topics around computing, but they are increasingly overwhelmed with teaching assignments and other obligations. I think the college will enable the types of interactions that are needed to foster new ideas.” Thinking about the impact on the student experience, Ozdaglar expects that the college will help students better navigate the computing landscape at MIT by creating clearer paths. She also notes that many students have passions beyond computer science, but realize the need to be adept in computing techniques and methodologies in order to pursue other interests, whether it be political science, economics, or urban science. “The idea for the college is to educate students who are fluent in computation, but at the same time, creatively apply computing with the methods and questions of the domain they are mostly interested in.” For Deputy Dean of Research Daniela Rus, who is also the director of CSAIL and the Andrew and Erna Viterbi Professor in EECS, developing research programs “that bring together MIT faculty and students from different units to advance computing and to make the world better through computing” is a top priority. She points to the recent launch of the MIT Air Force AI Innovation Accelerator, a collaboration between the MIT Schwarzman College of Computing and the U.S. Air Force focused on AI, as an example of the types of research projects the college can facilitate. “As humanity works to solve problems ranging from climate change to curing disease, removing inequality, ensuring sustainability, and eliminating poverty, computing opens the door to powerful new solutions,” says Rus. “And with the MIT Schwarzman College as our foundation, I believe MIT will be at the forefront of those solutions. Our scholars are laying theoretical foundations of computing and applying those foundations to big ideas in computing and across disciplines.” Habits of mind and action A critically important cross-cutting area is the Social and Ethical Responsibilities of Computing, which will facilitate the development of responsible “habits of mind and action” for those who create and deploy computing technologies, and the creation of technologies in the public interest. “The launch of the MIT Schwarzman College of Computing offers an extraordinary new opportunity for the MIT community to respond to today’s most consequential questions in ways that serve the common good,” says Melissa Nobles, professor of political science, the Kenan Sahin Dean of the MIT School of Humanities, Arts, and Social Sciences, and co-chair of the Task Force Working Group on Social Implications and Responsibilities of Computing. “As AI and other advanced technologies become ubiquitous in their influence and impact, touching nearly every aspect of life, we have increasingly seen the need to more consciously align powerful new technologies with core human values — integrating consideration of societal and ethical implications of new technologies into the earliest stages of their development. Asking, for example, of every new technology and tool: Who will benefit? What are the potential ecological and social costs? Will the new technology amplify or diminish human accomplishments in the realms of justice, democracy, and personal privacy? “As we shape the college, we are envisioning an MIT culture in which all of us are equipped and encouraged to think about such implications. In that endeavor, MIT’s humanistic disciplines will serve as deep resources for research, insight, and discernment. We also see an opportunity for advanced technologies to help solve political, economic, and social issues that trouble today’s world by integrating technology with a humanistic analysis of complex civilizational issues — among them climate change, the future of work, and poverty, issues that will yield only to collaborative problem-solving. It is not too much to say that human survival may rest on our ability to solve these problems via collective intelligence, designing approaches that call on the whole range of human knowledge.” Julie Shah, an associate professor in the Department of Aeronautics and Astronautics and head of the Interactive Robotics Group at CSAIL, who co-chaired the working group with Nobles and is now a member of the college leadership, adds that “traditional technologists aren’t trained to pause and envision the possible futures of how technology can and will be used. This means that we need to develop new ways of training our students and ourselves in forming new habits of mind and action so that we include these possible futures into our design.” The associate deans of Social and Ethical Responsibilities of Computing, Shah and David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, are designing a systemic framework for SERC that will not only effect change in computing education and research at MIT, but one that will also inform policy and practice in government and industry. Activities that are currently in development include multi-disciplinary curricula embedded in traditional computing and AI courses across all levels of instruction, the commission and curation of a series of case studies that will be modular and available to all via MIT’s open access channels, active learning projects, cross-disciplinary monthly convenings, public forums, and more.  “A lot of how we’ve been thinking about SERC components is building capacity with what we already have at the Institute as a very important first step. And that means how do we get people interacting in ways that can be a little bit different than what has been familiar, because I think there are a lot of shared goals among the MIT community, but the gears aren’t quite meshing yet. We want to further support collaborations that might cut across lines that otherwise might not have had much traffic between them,” notes Kaiser. Just the beginning While he’s excited by the progress made so far, Huttenlocher points out there will continue to be revisions made to the organizational structure of the college. “We are at the very beginning of the college, with a tremendous amount of excellence at MIT to build on, and with some clear needs and opportunities, but the landscape is changing rapidly and the college is very much a work in progress.” The college has other initiatives in the planning stages, such as the Center for Advanced Studies of Computing that will host fellows from inside and outside of MIT on semester- or year-long project-oriented programs in focused topic areas that could seed new research, scholarly, educational, or policy work. In addition, Huttenlocher is planning to launch a search for an assistant or associate dean of equity and inclusion, once the Institute Community and Equity Officer is in place, to focus on improving and creating programs and activities that will help broaden participation in computing classes and degree programs, increase the diversity of top faculty candidates in computing fields, and ensure that faculty search and graduate admissions processes have diverse slates of candidates and interviews. “The typical academic approach would be to wait until it’s clear what to do, but that would be a mistake. The way we’re going to learn is by trying and by being more flexible. That may be a more general attribute of the new era we’re living in, he says. “We don’t know what it’s going to look like years from now, but it’s going to be pretty different, and MIT is going to be shaping it.” The MIT Schwarzman College of Computing will be hosting a community forum on Wednesday, Feb. 12 at 2 p.m. in Room 10-250. Members from the MIT community are welcome to attend to learn more about the initial organizational structure of the college. MIT Schwarzman College of Computing leadership team (left to right) David Kaiser, Daniela Rus, Dan Huttenlocher, Julie Shah, and Asu Ozdaglar Photo: Sarah Bastille https://news.mit.edu/2020/college-for-the-computing-age-0204 With the initial organizational structure in place, the MIT Schwarzman College of Computing moves forward with implementation. Tue, 04 Feb 2020 12:30:01 -0500 https://news.mit.edu/2020/college-for-the-computing-age-0204 Terri Park | MIT Schwarzman College of Computing The mission of the MIT Stephen A. Schwarzman College of Computing is to address the opportunities and challenges of the computing age — from hardware to software to algorithms to artificial intelligence (AI) — by transforming the capabilities of academia in three key areas: supporting the rapid evolution and growth of computer science and AI; facilitating collaborations between computing and other disciplines; and focusing on social and ethical responsibilities of computing through combining technological approaches and insights from social science and humanities, and through engagement beyond academia. Since starting his position in August 2019, Daniel Huttenlocher, the inaugural dean of the MIT Schwarzman College of Computing, has been working with many stakeholders in designing the initial organizational structure of the college. Beginning with the College of Computing Task Force Working Group reports and feedback from the MIT community, the structure has been developed through an iterative process of draft plans yielding a 26-page document outlining the initial academic organization of the college that is designed to facilitate the college mission through improved coordination and evolution of existing computing programs at MIT, improved collaboration in computing across disciplines, and development of new cross-cutting activities and programs, notably in the social and ethical responsibilities of computing. “The MIT Schwarzman College of Computing is both bringing together existing MIT programs in computing and developing much-needed new cross-cutting educational and research programs,” says Huttenlocher. “For existing programs, the college helps facilitate coordination and manage the growth in areas such as computer science, artificial intelligence, data systems and society, and operations research, as well as helping strengthen interdisciplinary computing programs such as computational science and engineering. For new areas, the college is creating cross-cutting platforms for the study and practice of social and ethical responsibilities of computing, for multi-departmental computing education, and for incubating new interdisciplinary computing activities.” The following existing departments, institutes, labs, and centers are now part of the college: Department of Electrical Engineering and Computer (EECS), which has been reorganized into three overlapping sub-units of electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D), and is jointly part of the MIT Schwarzman College of Computing and School of Engineering; Operations Research Center (ORC), which is jointly part of the MIT Schwarzman College of Computing and MIT Sloan School of Management; Institute for Data, Systems, and Society (IDSS), which will be increasing its focus on the societal aspects of its mission while also continuing to support statistics across MIT, and including the Technology and Policy Program (TPP) and Sociotechnical Systems Research Center (SSRC); Center for Computational Science Engineering (CCSE), which is being renamed from the Center for Computational Engineering and broadening its focus in the sciences; Computer Science and Artificial Intelligence Laboratory (CSAIL); Laboratory for Information and Decision Systems (LIDS); and Quest for Intelligence. With the initial structure in place, Huttenlocher, the college leadership team, and the leaders of the academic units that are part of the college, in collaboration with departments in all five schools, are actively moving forward with curricular and programmatic development, including the launch of two new areas, the Common Ground for Computing Education and the Social and Ethical Responsibilities of Computing (SERC). Still in the early planning stages, these programs are the aspects of the college that are designed to cut across lines and involve a number of departments throughout MIT. Other programs are expected to be introduced as the college continues to take shape. “The college is an Institute-wide entity, working with and across all five schools,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science, who was part of the task force steering committee. “Its continued growth and focus depend greatly on the input of our MIT community, a process which began over a year ago. I’m delighted that Dean Huttenlocher and the college leadership team have engaged the community for collaboration and discussion around the plans for the college.” With these organizational changes, students, faculty, and staff in these units are members of the college, and in some cases, jointly with a school, as will be those who are engaged in the new cross-cutting activities in SERC and Common Ground. “A question we get frequently,” says Huttenlocher, “is how to apply to the college. As is the case throughout MIT, undergraduate admissions are handled centrally, and graduate admissions are handled by each individual department or graduate program.” Advancing computing Despite the unprecedented growth in computing, there remains substantial unmet demand for expertise. In academia, colleges and universities worldwide are faced with oversubscribed programs in computer science and the constant need to keep up with rapidly changing materials at both the graduate and undergraduate level. According to Huttenlocher, the computing fields are evolving at a pace today that is beyond the capabilities of current academic structures to handle. “As academics, we pride ourselves on being generators of new knowledge, but academic institutions themselves don’t change that quickly. The rise of AI is probably the biggest recent example of that, along with the fact that about 40 percent of MIT undergraduates are majoring in computer science, where we have 7 percent of the MIT faculty.” In order to help meet this demand, MIT is increasing its academic capacity in computing and AI with 50 new faculty positions — 25 will be core computing positions in CS, AI, and related areas, and 25 will be shared jointly with departments. Searches are now active to recruit core faculty in CS and AI+D, and for joint faculty with MIT Philosophy, the Department of Brain and Cognitive Sciences, and several interdisciplinary institutes. The new shared faculty searches will largely be conducted around the concept of “clusters” to build capacity at MIT in important computing areas that cut across disciplines, departments, and schools. Huttenlocher, the provost, and the five school deans will work to identify themes based on input from departments so that recruiting can be undertaken during the next academic year. Cross-cutting collaborations in computing Building on the history of strong faculty participation in interdepartmental labs, centers, and initiatives, the MIT Schwarzman College of Computing provides several forms of membership in the college based on cross-cutting research, teaching, or external engagement activities. While computing is affecting intellectual inquiry in almost every discipline, Huttenlocher is quick to stress that “it’s bi-directional.” He notes that existing collaborations across various schools and departments, such as MIT Digital Humanities, as well as opportunities for new such collaborations, are key to the college mission because in the same way that “computing is changing thinking in the disciplines; the disciplines are changing the way people do computing.” Under the leadership of Asu Ozdaglar, the deputy dean of academics and department head of EECS, the college is developing the Common Ground for Computing Education, an interdepartmental teaching collaborative that will facilitate the offering of computing classes and coordination of computing-related curricula across academic units. The objectives of this collaborative are to provide opportunities for faculty across departments to work together, including co-teaching classes, creating new undergraduate majors or minors such as in AI+D, as well as facilitating undergraduate blended degrees such as 6-14 (Computer Science, Economics, and Data Science), 6-9 (Computation and Cognition), 11-6 (Urban Science and Planning with Computer Science), 18-C (Mathematics with Computer Science), and others. “It is exciting to bring together different areas of computing with methodological and substantive commonalities as well as differences around one table,” says Ozdaglar. “MIT faculty want to collaborate in topics around computing, but they are increasingly overwhelmed with teaching assignments and other obligations. I think the college will enable the types of interactions that are needed to foster new ideas.” Thinking about the impact on the student experience, Ozdaglar expects that the college will help students better navigate the computing landscape at MIT by creating clearer paths. She also notes that many students have passions beyond computer science, but realize the need to be adept in computing techniques and methodologies in order to pursue other interests, whether it be political science, economics, or urban science. “The idea for the college is to educate students who are fluent in computation, but at the same time, creatively apply computing with the methods and questions of the domain they are mostly interested in.” For Deputy Dean of Research Daniela Rus, who is also the director of CSAIL and the Andrew and Erna Viterbi Professor in EECS, developing research programs “that bring together MIT faculty and students from different units to advance computing and to make the world better through computing” is a top priority. She points to the recent launch of the MIT Air Force AI Innovation Accelerator, a collaboration between the MIT Schwarzman College of Computing and the U.S. Air Force focused on AI, as an example of the types of research projects the college can facilitate. “As humanity works to solve problems ranging from climate change to curing disease, removing inequality, ensuring sustainability, and eliminating poverty, computing opens the door to powerful new solutions,” says Rus. “And with the MIT Schwarzman College as our foundation, I believe MIT will be at the forefront of those solutions. Our scholars are laying theoretical foundations of computing and applying those foundations to big ideas in computing and across disciplines.” Habits of mind and action A critically important cross-cutting area is the Social and Ethical Responsibilities of Computing, which will facilitate the development of responsible “habits of mind and action” for those who create and deploy computing technologies, and the creation of technologies in the public interest. “The launch of the MIT Schwarzman College of Computing offers an extraordinary new opportunity for the MIT community to respond to today’s most consequential questions in ways that serve the common good,” says Melissa Nobles, professor of political science, the Kenan Sahin Dean of the MIT School of Humanities, Arts, and Social Sciences, and co-chair of the Task Force Working Group on Social Implications and Responsibilities of Computing. “As AI and other advanced technologies become ubiquitous in their influence and impact, touching nearly every aspect of life, we have increasingly seen the need to more consciously align powerful new technologies with core human values — integrating consideration of societal and ethical implications of new technologies into the earliest stages of their development. Asking, for example, of every new technology and tool: Who will benefit? What are the potential ecological and social costs? Will the new technology amplify or diminish human accomplishments in the realms of justice, democracy, and personal privacy? “As we shape the college, we are envisioning an MIT culture in which all of us are equipped and encouraged to think about such implications. In that endeavor, MIT’s humanistic disciplines will serve as deep resources for research, insight, and discernment. We also see an opportunity for advanced technologies to help solve political, economic, and social issues that trouble today’s world by integrating technology with a humanistic analysis of complex civilizational issues — among them climate change, the future of work, and poverty, issues that will yield only to collaborative problem-solving. It is not too much to say that human survival may rest on our ability to solve these problems via collective intelligence, designing approaches that call on the whole range of human knowledge.” Julie Shah, an associate professor in the Department of Aeronautics and Astronautics and head of the Interactive Robotics Group at CSAIL, who co-chaired the working group with Nobles and is now a member of the college leadership, adds that “traditional technologists aren’t trained to pause and envision the possible futures of how technology can and will be used. This means that we need to develop new ways of training our students and ourselves in forming new habits of mind and action so that we include these possible futures into our design.” The associate deans of Social and Ethical Responsibilities of Computing, Shah and David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, are designing a systemic framework for SERC that will not only effect change in computing education and research at MIT, but one that will also inform policy and practice in government and industry. Activities that are currently in development include multi-disciplinary curricula embedded in traditional computing and AI courses across all levels of instruction, the commission and curation of a series of case studies that will be modular and available to all via MIT’s open access channels, active learning projects, cross-disciplinary monthly convenings, public forums, and more.  “A lot of how we’ve been thinking about SERC components is building capacity with what we already have at the Institute as a very important first step. And that means how do we get people interacting in ways that can be a little bit different than what has been familiar, because I think there are a lot of shared goals among the MIT community, but the gears aren’t quite meshing yet. We want to further support collaborations that might cut across lines that otherwise might not have had much traffic between them,” notes Kaiser. Just the beginning While he’s excited by the progress made so far, Huttenlocher points out there will continue to be revisions made to the organizational structure of the college. “We are at the very beginning of the college, with a tremendous amount of excellence at MIT to build on, and with some clear needs and opportunities, but the landscape is changing rapidly and the college is very much a work in progress.” The college has other initiatives in the planning stages, such as the Center for Advanced Studies of Computing that will host fellows from inside and outside of MIT on semester- or year-long project-oriented programs in focused topic areas that could seed new research, scholarly, educational, or policy work. In addition, Huttenlocher is planning to launch a search for an assistant or associate dean of equity and inclusion, once the Institute Community and Equity Officer is in place, to focus on improving and creating programs and activities that will help broaden participation in computing classes and degree programs, increase the diversity of top faculty candidates in computing fields, and ensure that faculty search and graduate admissions processes have diverse slates of candidates and interviews. “The typical academic approach would be to wait until it’s clear what to do, but that would be a mistake. The way we’re going to learn is by trying and by being more flexible. That may be a more general attribute of the new era we’re living in, he says. “We don’t know what it’s going to look like years from now, but it’s going to be pretty different, and MIT is going to be shaping it.” The MIT Schwarzman College of Computing will be hosting a community forum on Wednesday, Feb. 12 at 2 p.m. in Room 10-250. Members from the MIT community are welcome to attend to learn more about the initial organizational structure of the college. MIT Schwarzman College of Computing leadership team (left to right) David Kaiser, Daniela Rus, Dan Huttenlocher, Julie Shah, and Asu Ozdaglar Photo: Sarah Bastille https://news.mit.edu/2020/mit-launches-masters-data-economics-development-policy-0204 The first cohort of 22 students from 14 countries share a common ambition: harnessing data to help others. Tue, 04 Feb 2020 09:00:00 -0500 https://news.mit.edu/2020/mit-launches-masters-data-economics-development-policy-0204 Abdul Latif Jameel Poverty Action Lab (J-PAL) This week, the first cohort of 22 students begin classes in MIT’s new master’s program in Data, Economics, and Development Policy (DEDP). The graduate program was created jointly by MIT’s Department of Economics and the Abdul Latif Jameel Poverty Action Lab (J-PAL), a research center at MIT led by professors Abhijit Banerjee, Esther Duflo, and Benjamin Olken. Banerjee and Duflo are co-recipients of the 2019 Nobel Memorial Prize in Economics.  The 22 students beginning the master’s program this week hail from 14 countries around the world, including Brazil, India, Jordan, Lithuania, Mexico, Nigeria, the United States, and Zimbabwe.  The students are pioneers of a new approach to higher education: College degrees and standardized test scores are not required for admission. Instead, applicants prove their readiness through their performance in online MITx MicroMasters courses, completing weekly assignments and taking proctored final exams.  The program’s unique admissions process reflects Banerjee, Duflo, and Olken’s ambition to democratize higher education, leveling the playing field to enable students from all backgrounds to succeed. The makeup of the cohort reflects this nontraditional approach to admissions. Students joining the Data, Economics, and Development Policy program possess a range of professional backgrounds, with experience in finance, management consulting, and government; and with organizations like UNICEF, Google, and The New York Times — one incoming student is even joining directly from high school.  Applying data for better public policy The master’s program combines five challenging MicroMasters courses, one semester of on-campus learning, and a summer capstone experience to provide students with an accessible yet rigorous academic experience. The curriculum is designed to equip students with the tools to apply data for more effective decision-making in public policy, with a focus on social policies that target poverty alleviation.  This includes coursework in microeconomics, econometrics, political economy, psychology, data science, and more — all designed to provide a practical, well-rounded graduate education. Many students hope to apply the knowledge they gain in the DEDP program to improve the lives of people in their home countries. Helena Lima, an incoming student from Brazil, plans to return to Brazil after graduation. “My goal [after completing this program] is to move the needle in Brazilian public education, contributing to increase access to high-quality schools for the most vulnerable people and communities,” says Helena.  Lovemore Mawere, an incoming student from Zimbabwe, shares this sentiment. “I intend to return home to Africa after the master’s program. I believe the experience and the skills gained will embolden me to take action and lead the fight against poverty.” Expanding access for all students The blended online and in-person structure of the program means that students spend just one semester on campus at MIT, but program administrators recognize that costs of tuition and living expenses can still be prohibitive. Administrators say that they are working on bringing these costs down and providing scholarship funding.  “We’ve partnered with the Hewlett Foundation to provide scholarships for students from sub-Saharan Africa, and are actively seeking other funding partners who share our vision,” says Maya Duru, associate director of education at J-PAL. “The individuals who apply to this program are incredibly smart, motivated, and resourceful. We want to work with donors to establish a sustainable scholarship fund to ensure that finances are never a barrier to participation.”  Esther Duflo, the MIT professor and Nobel laureate who helped create the program, emphasized the critical importance of the program’s mission.  “It is more important now than ever to ensure that the next generation of leaders understand how best to use data to inform decisions, especially when it comes to public policy,” says Duflo. “We are preparing our students to succeed in future leadership positions in government, NGOs, and the private sector — and, hopefully, to help shift their institutional cultures toward a more data-driven approach to policy.” The first students to enroll in MIT’s new MicroMaster Program in Data, Economics, and Development Policy program arrived at MIT in January. Photo: Amanda Kohn/J-PAL More

Many analysts have predicted an explosion in the number of industrial “internet of things” (IoT) devices that will come online over the next decade. Sensors play a big role in those forecasts.
Unfortunately, sensors come with their own drawbacks, many of which are due to the limited energy supply and finite lifetime of their batteries.
Now the startup Everactive has developed industrial sensors that run around the clock, require minimal maintenance, and can last over 20 years. The company created the sensors not by redesigning its batteries, but by eliminating them altogether.
The key is Everactive’s ultra-low-power integrated circuits, which harvest energy from sources like indoor light and vibrations to generate data. The sensors continuously send that data to Everactive’s cloud-based dashboard, which gives users real time insights, analysis, and alerts to help them leverage the full power of industrial IoT devices.
“It’s all enabled by the ultra-low-power chips that support continuous monitoring,” says Everactive Co-Chief Technology Officer David Wentzloff SM ’02, PhD ’07. “Because our source of power is unlimited, we’re not making tradeoffs like keeping radios off or doing something else [limiting] to save battery life.”
Everactive builds finished products on top of its chips that customers can quickly deploy in large numbers. Its first product monitors steam traps, which release condensate out of steam systems. Such systems are used in a variety of industries, and Everactive’s customers include companies in sectors like oil and gas, paper, and food production. Everactive has also developed a sensor to monitor rotating machinery, like motors and pumps, that runs on the second generation of its battery-free chips.
By avoiding the costs and restrictions associated with other sensors, the company believes it’s well-positioned to play a role in the IoT-powered transition to the factory of the future.
“This is technology that’s totally maintenance free, with no batteries, powered by harvested energy, and always connected to the cloud. There’s so many things you can do with that, it’s hard to wrap your head around,” Wentzloff says.
Breaking free from batteries
Wentzloff and his Everactive co-founder and co-CTO Benton Calhoun SM ’02, PhD ’06 have been working on low-power circuit design for more than a decade, beginning with their time at MIT. They both did their PhD work in the lab of Anantha Chandrakasan, who is currently the Vannevar Bush Professor of Electrical Engineering and Computer Science and the dean of MIT’s School of Engineering. Calhoun’s research focused on low-power digital circuits and memory while Wentzloff’s focused on low power radios.
After earning their PhDs, both men became assistant professors at the schools they attended as undergraduates — Wentzloff at the University of Michigan and Calhoun at the University of Virginia — where they still teach today. Even after settling in different parts of the country, they continued collaborating, applying for joint grants and building circuit-based systems that combined their areas of research.
The collaboration was not an isolated incident: The founders have maintained relationships with many of their contacts from MIT.
“To this day I stay in touch with my colleagues and professors,” Wentzloff says. “It’s a great group to be associated with, especially when you talk about the integrated circuit space. It’s a great community, and I really value and appreciate that experience and those connections that have come out of it. That’s far an away the longest impression MIT has left on my career, those people I continue to stay in touch with. We’re all helping each other out.”
Wentzloff and Calhoun’s academic labs eventually created a battery-free physiological monitor that could track a user’s movement, temperature, heart rate, and other signals and send that data to a phone, all while running on energy harvested from body heat.
“That’s when we decided we should look at commercializing this technology,” Wentzloff says.
In 2014, they partnered with semiconductor industry veteran Brendan Richardson to launch the company, originally called PsiKick.
In the beginning, when Wentzloff describes the company as “three guys and a dog in a garage,” the founders sought to reimagine circuit designs that included features of full computing systems like sensor interfaces, processing power, memory, and radio signals. They also needed to incorporate energy harvesting mechanisms and power management capabilities.
“We wiped the slate clean and had a fresh start,” Wentzloff recalls.
The founders initially attempted to sell their chips to companies to build solutions on top of, but they quickly realized the industry wasn’t familiar enough with battery-free chips.
“There’s an education level to it, because there’s a generation of engineers used to thinking of systems design with battery-operated chips,” Wentzloff says.
The learning curve led the founders to start building their own solutions for customers. Today Everactive offers its sensors as part of a wider service that incorporates wireless networks and data analytics.
The company’s sensors can be powered by small vibrations, lights inside a factory as dim as 100 lux, and heat differentials below 10 degrees Fahrenheit. The devices can sense temperature, acceleration, vibration, pressure, and more.
The company says its sensors cost significantly less to operate than traditional sensors and avoid the maintenance headache that comes with deploying thousands of battery-powered devices.
For instance, Everactive considered the cost of deploying 10,000 traditional sensors. Assuming a three-year battery life, the customer would need to replace an average of 3,333 batteries each year, which comes out to more than nine a day.
The next technological revolution
By saving on maintenance and replacement costs, Everactive customers are able to deploy more sensors. That, combined with the near-continuous operation of those sensors, brings a new level of visibility to operations.
“[Removing restrictions on sensor installations] starts to give you a sixth sense, if you will, about how your overall operations are running,” Calhoun says. “That’s exciting. Customers would like to wave a magic wand and know exactly what’s going on wherever they’re interested. The ability to deploy tens of thousands of sensors gets you close to that magic wand.”
With thousands of Everactive’s steam trap sensors already deployed, Wentzloff believes its sensors for motors and other rotating machinery will make an even bigger impact on the IoT market.
Beyond Everactive’s second generation of products, the founders say their sensors are a few years away from being translucent, flexible, and the size of a postage stamp. At that point customers will simply need to stick the sensors onto machines to start generating data. Such ease of installation and use would have implications far beyond the factory floor.
“You hear about smart transportation, smart agriculture, etc.,” Calhoun says. “IoT has this promise to make all of our environments smart, meaning there’s an awareness of what’s going on and use of that information to have these environments behave in ways that anticipate our needs and are as efficient as possible. We believe battery-less sensing is required and inevitable to bring about that vision, and we’re excited to be a part of that next computing revolution.”

Topics: Research, Computer science and technology, internet of things, Data, Electrical Engineering & Computer Sciences (EECS), School of Engineering, Manufacturing, Startups, Innovation and Entrepreneurship (I&E), Batteries, Energy, Energy storage, Alumni/ae, electronics More

Analysis shows requiring masks for public-facing U.S. business employees on April 1 would have saved tens of thousands of lives. More

The School of Engineering has announced that MIT has granted tenure to eight members of its faculty in the departments of Civil and Environmental Engineering, Chemical Engineering, Electrical Engineering and Computer Science, Mechanical Engineering, and Nuclear Science and Engineering.
“This year’s newly tenured faculty in the School of Engineering are truly inspiring,” says Anantha P. Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Their dedication to research and teaching drives novel solutions urgently needed to advance their fields.”
This year’s newly tenured associate professors are:
Lydia Bourouiba, in the Department of Civil and Environmental Engineering and the Institute for Medical Engineering and Science, focuses her expertise as a physical applied mathematician on problems at the interface of fluid dynamics and infectious disease transmission. Her work leverages advanced fluid dynamic experiments at various scales, algorithms, and mathematical modeling to understand the physical mechanisms shaping disease transmission dynamics, epidemics, and pandemics in human, animal, and plant populations. Motivated by problems in these application domains, her work elucidates fundamental multiscale dynamics of fluid fragmentation, mixing, and transport processes where interfacial, multi-phase, biological, and complex fluids and flows are determining pathogen dispersal and persistence in a range of environments.
Fikile Brushett, the Cecil and Ida Green Career Development Professor in the Department of Chemical Engineering, focuses his research on advancing the science and engineering of electrochemical technologies for a sustainable energy economy. He is especially fascinated by the fundamental processes that define the performance, cost, and lifetime of present-day and future electrochemical systems.
Thomas Heldt, in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, focuses his research on signal processing, mathematical modeling, and model identification to understand the physiology of the injured brain and to support real-time clinical decision-making, monitoring of disease progression, and titration of therapy. His research is conducted in close collaboration with clinicians from Boston-area hospitals — particularly in emergency, neonatal and neurocritical care — where his team is integrally involved in designing and deploying high-fidelity data-acquisition systems and in collecting clinical data. 
Asegun Henry is the Robert N. Noyce Career Development Professor in the Department of Mechanical Engineering. His primary research is in heat transfer, with an emphasis on understanding the science of energy transport, storage and conversion at the atomic level, along with the development of new industrial-scale energy technologies to mitigate climate change. He has made significant advances and contributions to several fields within energy and heat transfer, namely: solar fuels and thermochemistry, phonon transport in disordered materials, phonon transport at interfaces, and he has developed the highest-temperature pump on record, which used an all-ceramic mechanical pump to pump liquid metal above 1,400 degrees Celsius.
William Oliver, in the Department of Electrical Engineering and Computer Science, works with the Quantum Information and Integrated Nanosystems Group at Lincoln Laboratory and the Engineering Quantum Systems Group at MIT, where he provides programmatic and technical leadership for programs related to the development of quantum and classical high-performance computing technologies for quantum information science applications. His interests include the materials growth, fabrication, design, and control of superconducting quantum processors, as well as the development of cryogenic packaging and control electronics involving cryogenic CMOS and single-flux quantum digital logic. He is director of the Center for Quantum Engineering and associate director of the Research Laboratory of Electronics.
Michael Short, the Class of 1942 Career Development Professor in the Department of Nuclear Science and Engineering, develops new materials and measurement methods to usher in the next generation of safe and scalable nuclear power. He is currently focused on choosing and proving structural materials for fusion reactors, creating tools to measure tiny amounts of radiation damage for nuclear non-proliferation, and stopping corrosion and fouling in the most extreme energy production environments.
Vivienne Sze, in the Department of Electrical Engineering and Computer Science, focuses her research on designing and implementing computing systems that enable energy-efficient machine learning, computer vision, and video compression for a wide range of applications, including autonomous navigation, digital health, and the internet of things. In particular, she is interested in the joint design of algorithms, architectures, circuits, and systems to enable optimal tradeoffs between energy consumption, speed, and quality of results. 
Caroline Uhler, in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, focuses her research at the intersection of machine learning, statistics, and genomics. In particular, she is interested in obtaining a better understanding of genome regulation by developing machine learning methods that can integrate different data modalities, including interventional data, and bridge the gap from predictive to causal modeling.

Topics: School of Engineering, Civil and environmental engineering, Electrical engineering and computer science (EECS), Mechanical engineering, Nuclear science and engineering, Institute for Medical Engineering and Science (IMES), Lincoln Laboratory, Research Laboratory of Electronics, MIT Schwarzman College of Computing, Faculty, Chemical engineering, IDSS, Awards, honors and fellowships More

On June 18, 609 learners celebrated the completion of MITx MicroMasters programs in Data, Economics, and Development Policy (DEDP), Principles of Manufacturing, and Statistics and Data Science in an online event hosted by MIT Open Learning. With Vice President for Open Learning Professor Sanjay Sarma presiding, the celebration emphasized the credential holders’ tenacity and potential to transform their industries and communities.
This is the first time cohorts from these three programs have been recognized through a completion ceremony, bringing together learners from 82 countries who earned their credentials between 2018 and 2020. Housed at the Office of Open Learning, the MicroMasters programs are created in conjunction with departments, labs, and centers all over MIT: DEDP is offered jointly through the Department of Economics and the Abdul Latif Jameel Poverty Action Lab (J-PAL), Principles of Manufacturing through the Department of Mechanical Engineering, and Statistics and Data Science through the Institute for Data, Systems, and Society (IDSS).
“Learning online requires a lot of self-discipline and perseverance,” notes Esther Duflo, the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics. “You have really achieved something very difficult and quite remarkable, that shows both your talent and your commitment.”
Opening doors, broadening perspectives
The ceremony, like the certificate itself, means something different to each recipient. For some, it’s a private achievement, signaling a mastery of a subject that holds deep personal significance. For others, it’s a momentous step in reaching career goals. Still others view the MicroMasters program as a route toward an education they couldn’t otherwise achieve: 70 percent of DEDP learners, for example, come from middle- to low-income countries.
The MicroMasters learners honored at this year’s ceremony represent a wide variety of professionals at all stages of their careers. David Bruce, a liver transplant surgeon at the Ochsner Clinic in New Orleans, Louisiana, enrolled in the Statistics and Data Science program out of interest in the subject, and is now applying his new knowledge in transplant informatics. Fabio Castro, a learner in Brazil who completed the same program, was inspired to apply for MIT’s PhD program in civil engineering, and he will matriculate in the fall. 
Some of the ceremony’s honorees were already avid online learners before beginning their MicroMasters journey. Linxi Wang, a DEDP learner currently based in the United States, described how valuable it is to be connected to a global network of like-minded professionals: “One thing I absolutely love and couldn’t find anywhere else is the friendly community we’ve built.”
Badri Ratnam, who received the Principles of Manufacturing credential this year, had earned upwards of 25 MITx certificates in several subjects, and was thrilled to discover that he could use his learning to advance his engineering career. “When I saw that MITx was offering a mechanical engineering-related MicroMasters, I jumped at the opportunity,” he says. “The value of this program to me is that it helped me understand the challenges in bringing a prototype product to the world — I’m engaged in one such project at work as we speak.” Now having completed his credential, Ratnam hopes to continue his studies, perhaps earning dual degrees in manufacturing and supply chain management.
Many credential holders have been able to apply their knowledge to pressing global issues. Australia-based learner David Fong described how the DEDP program helped inform his work in child development and health-care screening with the nonprofit Spur Afrika in Nairobi, Kenya. Eva Flonner, a learner in Austria, could not have found a more urgent use for her new skills in Statistics and Data Science: “The MIT MicroMasters program really changed my career, since I’m now responsible for data science tasks linked to the corona crisis,” she says.
A new way forward for global education
In addition celebrating the personal achievements of individual credential holders, the ceremony is testament to the possibilities offered by a new way forward in education. MIT launched the MicroMasters program in 2016, the first of its kind in the world. It began as a means of disrupting the traditional admissions process for the master’s degree program in supply chain management: anyone who earned the online credential, requiring the successful completion of a curated suite of MITx courses, would be eligible to finish their degree on campus, without needing to meet any other admissions criteria. 
The microcredential model has been replicated at dozens of global universities in recent years, both as an alternate graduate admissions route and as a means of awarding respected professional credentials for high-demand fields. In his remarks, Professor Devavrat Shah, director of MIT’s Statistics and Data Science Center, commented on how much the field of higher education can learn from implementing microcredential programs: “[MicroMasters learners] have become model students for us as we, universities across the globe, grapple with Covid-19 and think about how we deal with blended and online education,” he said.
The MIT MicroMasters program continues to grow, with credentials awarded to 20,852 individual learners in four different disciplines, and with pathways to graduate degrees at collaborating universities around the world. A fifth program, the new MicroMasters in Finance from MIT Sloan School of Management, will begin running courses in September. 
David Hardt, professor of mechanical engineering, echoed the sentiments of all the MicroMasters ceremony speakers, expressing a wish that each credential holder will use their new skills and knowledge to become a leader in their field, “someone people will look to” for knowledge and guidance. Remarking on how closely the MicroMasters mirrors the rigor of a residential MIT graduate program, Hardt said, “You’ve done a marvelous thing.” 
Says Professor Krishna Rajagopal, dean for digital learning, “This celebration served as an affirmation that amidst so much uncertainty, people can still accomplish great things. I have no doubt that MicroMasters learners will help lead us through these challenging times and into a brighter future.”

Topics: Office of Open Learning, Economics, MITx, Abdul Latif Jameel Poverty Action Lab (J-PAL), Mechanical engineering, IDSS, Sloan School of Management, Center for Transportation and Logistics, Learning, Massive open online courses (MOOCs), Education, teaching, academics, School of Engineering, School of Humanities Arts and Social Sciences More

Uncertainty about the course of the Covid-19 pandemic continues, with more than 2,500,000 known cases and 126,000 deaths in the United States alone. How to contain the virus, limit its damage, and address the deep-rooted health and racial inequalities it has exposed are now urgent topics for policymakers. Earlier this spring, 300 data scientists and […] More