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    A universal system for decoding any type of data sent across a network

    Every piece of data that travels over the internet — from paragraphs in an email to 3D graphics in a virtual reality environment — can be altered by the noise it encounters along the way, such as electromagnetic interference from a microwave or Bluetooth device. The data are coded so that when they arrive at their destination, a decoding algorithm can undo the negative effects of that noise and retrieve the original data.

    Since the 1950s, most error-correcting codes and decoding algorithms have been designed together. Each code had a structure that corresponded with a particular, highly complex decoding algorithm, which often required the use of dedicated hardware.

    Researchers at MIT, Boston University, and Maynooth University in Ireland have now created the first silicon chip that is able to decode any code, regardless of its structure, with maximum accuracy, using a universal decoding algorithm called Guessing Random Additive Noise Decoding (GRAND). By eliminating the need for multiple, computationally complex decoders, GRAND enables increased efficiency that could have applications in augmented and virtual reality, gaming, 5G networks, and connected devices that rely on processing a high volume of data with minimal delay.

    The research at MIT is led by Muriel Médard, the Cecil H. and Ida Green Professor in the Department of Electrical Engineering and Computer Science, and was co-authored by Amit Solomon and Wei Ann, both graduate students at MIT; Rabia Tugce Yazicigil, assistant professor of electrical and computer engineering at Boston University; Arslan Riaz and Vaibhav Bansal, both graduate students at Boston University; Ken R. Duffy, director of the Hamilton Institute at the National University of Ireland at Maynooth; and Kevin Galligan, a Maynooth graduate student. The research will be presented at the European Solid-States Device Research and Circuits Conference next week.

    Focus on noise

    One way to think of these codes is as redundant hashes (in this case, a series of 1s and 0s) added to the end of the original data. The rules for the creation of that hash are stored in a specific codebook.

    As the encoded data travel over a network, they are affected by noise, or energy that disrupts the signal, which is often generated by other electronic devices. When that coded data and the noise that affected them arrive at their destination, the decoding algorithm consults its codebook and uses the structure of the hash to guess what the stored information is.

    Instead, GRAND works by guessing the noise that affected the message, and uses the noise pattern to deduce the original information. GRAND generates a series of noise sequences in the order they are likely to occur, subtracts them from the received data, and checks to see if the resulting codeword is in a codebook.

    While the noise appears random in nature, it has a probabilistic structure that allows the algorithm to guess what it might be.

    “In a way, it is similar to troubleshooting. If someone brings their car into the shop, the mechanic doesn’t start by mapping the entire car to blueprints. Instead, they start by asking, ‘What is the most likely thing to go wrong?’ Maybe it just needs gas. If that doesn’t work, what’s next? Maybe the battery is dead?” Médard says.

    Novel hardware

    The GRAND chip uses a three-tiered structure, starting with the simplest possible solutions in the first stage and working up to longer and more complex noise patterns in the two subsequent stages. Each stage operates independently, which increases the throughput of the system and saves power.

    The device is also designed to switch seamlessly between two codebooks. It contains two static random-access memory chips, one that can crack codewords, while the other loads a new codebook and then switches to decoding without any downtime.

    The researchers tested the GRAND chip and found it could effectively decode any moderate redundancy code up to 128 bits in length, with only about a microsecond of latency.

    Médard and her collaborators had previously demonstrated the success of the algorithm, but this new work showcases the effectiveness and efficiency of GRAND in hardware for the first time.

    Developing hardware for the novel decoding algorithm required the researchers to first toss aside their preconceived notions, Médard says.

    “We couldn’t go out and reuse things that had already been done. This was like a complete whiteboard. We had to really think about every single component from scratch. It was a journey of reconsideration. And I think when we do our next chip, there will be things with this first chip that we’ll realize we did out of habit or assumption that we can do better,” she says.

    A chip for the future

    Since GRAND only uses codebooks for verification, the chip not only works with legacy codes but could also be used with codes that haven’t even been introduced yet.

    In the lead-up to 5G implementation, regulators and communications companies struggled to find consensus as to which codes should be used in the new network. Regulators ultimately chose to use two types of traditional codes for 5G infrastructure in different situations. Using GRAND could eliminate the need for that rigid standardization in the future, Médard says.

    The GRAND chip could even open the field of coding to a wave of innovation.

    “For reasons I’m not quite sure of, people approach coding with awe, like it is black magic. The process is mathematically nasty, so people just use codes that already exist. I’m hoping this will recast the discussion so it is not so standards-oriented, enabling people to use codes that already exist and create new codes,” she says.

    Moving forward, Médard and her collaborators plan to tackle the problem of soft detection with a retooled version of the GRAND chip. In soft detection, the received data are less precise.

    They also plan to test the ability of GRAND to crack longer, more complex codes and adjust the structure of the silicon chip to improve its energy efficiency.

    The research was funded by the Battelle Memorial Institute and Science Foundation of Ireland. More

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    MIT welcomes nine MLK Visiting Professors and Scholars for 2021-22

    In its 31st year, the Martin Luther King Jr. (MLK) Visiting Professors and Scholars Program will host nine outstanding scholars from across the Americas. The flagship program honors the life and legacy of Martin Luther King Jr. by increasing the presence and recognizing the contributions of underrepresented minority scholars at MIT. Throughout the year, the cohort will enhance their scholarship through intellectual engagement with the MIT community and enrich the cultural, academic, and professional experience of students.

    The 2021-22 scholars

    Sanford Biggers is an interdisciplinary artist hosted by the Department of Architecture. His work is an interplay of narrative, perspective, and history that speaks to current social, political, and economic happenings while examining their contexts. His diverse practice positions him as a collaborator with the past through explorations of often-overlooked cultural and political narratives from American history. Through collaboration with his faculty host, Brandon Clifford, he will spend the year contributing to projects with Architecture; Art, Culture and Technology; the Transmedia Storytelling initiatives; and community workshops and engagement with local K-12 education.

    Kristen Dorsey is an assistant professor of engineering at Smith College. She will be hosted by the Program in Media Arts and Sciences at the MIT Media Lab. Her research focuses on the fabrication and characterization of microscale sensors and microelectromechanical systems. Dorsey tries to understand “why things go wrong” by investigating device reliability and stability. At MIT, Dorsey is interested in forging collaborations to consider issues of access and equity as they apply to wearable health care devices.

    Omolola “Lola” Eniola-Adefeso is the associate dean for graduate and professional education and associate professor of chemical engineering at the University of Michigan. She will join MIT’s Department of Chemical Engineering (ChemE). Eniola-Adefeso will work with Professor Paula Hammond on developing electrostatically assembled nanoparticle coatings that enable targeting of specific immune cell types. A co-founder and chief scientific officer of Asalyxa Bio, she is interested in the interactions between blood leukocytes and endothelial cells in vessel lumen lining, and how they change during inflammation response. Eniola-Adefeso will also work with the Diversity in Chemical Engineering (DICE) graduate student group in ChemE and the National Organization of Black Chemists and Chemical Engineers.

    Robert Gilliard Jr. is an assistant professor of chemistry at the University of Virginia and will join the MIT chemistry department, working closely with faculty host Christopher Cummins. His research focuses on various aspects of group 15 element chemistry. He was a founding member of the National Organization of Black Chemists and Chemical Engineers UGA section, and he has served as an American Chemical Society (ACS) Bridge Program mentor as well as an ACS Project Seed mentor. Gilliard has also collaborated with the Cleveland Public Library to expose diverse young scholars to STEM fields.

    Valencia Joyner Koomson ’98, MNG ’99 will return for the second semester of her appointment this fall in MIT’s Department of Electrical Engineering and Computer Science. Based at Tufts University, where she is an associate professor in the Department of Electrical and Computer Engineering, Koomson has focused her research on microelectronic systems for cell analysis and biomedical applications. In the past semester, she has served as a judge for the Black Alumni/ae of MIT Research Slam and worked closely with faculty host Professor Akintunde Akinwande.

    Luis Gilberto Murillo-Urrutia will continue his appointment in MIT’s Environmental Solutions Initiative. He has 30 years of experience in public policy design, implementation, and advocacy, most notably in the areas of sustainable regional development, environmental protection and management of natural resources, social inclusion, and peace building. At MIT, he has continued his research on environmental justice, with a focus on carbon policy and its impacts on Afro-descendant communities in Colombia.

    Sonya T. Smith was the first female professor of mechanical engineering at Howard University. She will join the Department of Aeronautics and Astronautics at MIT. Her research involves computational fluid dynamics and thermal management of electronics for air and space vehicles. She is looking forward to serving as a mentor to underrepresented students across MIT and fostering new research collaborations with her home lab at Howard.

    Lawrence Udeigwe is an associate professor of mathematics at Manhattan College and will join MIT’s Department of Brain and Cognitive Sciences. He plans to co-teach a graduate seminar course with Professor James DiCarlo to explore practical and philosophical questions regarding the use of simulations to build theories in neuroscience. Udeigwe also leads the Lorens Chuno group; as a singer-songwriter, his work tackles intersectionality issues faced by contemporary Africans.

    S. Craig Watkins is an internationally recognized expert in media and a professor at the University of Texas at Austin. He will join MIT’s Institute for Data, Systems, and Society to assist in researching the role of big data in enabling deep structural changes with regard to systemic racism. He will continue to expand on his work as founding director of the Institute for Media Innovation at the University of Texas at Austin, exploring the intersections of critical AI studies, critical race studies, and design. He will also work with MIT’s Center for Advanced Virtuality to develop computational systems that support social perspective-taking.

    Community engagement

    Throughout the 2021-22 academic year, MLK professors and scholars will be presenting their research at a monthly speaker series. Events will be held in an in-person/Zoom hybrid environment. All members of the MIT community are encouraged to attend and hear directly from this year’s cohort of outstanding scholars. To hear more about upcoming events, subscribe to their mailing list.

    On Sept. 15, all are invited to join the Institute Community and Equity Office in welcoming the scholars to campus by attending a welcome luncheon. More

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    Using adversarial attacks to refine molecular energy predictions

    Neural networks (NNs) are increasingly being used to predict new materials, the rate and yield of chemical reactions, and drug-target interactions, among others. For these applications, they are orders of magnitude faster than traditional methods such as quantum mechanical simulations. 

    The price for this agility, however, is reliability. Because machine learning models only interpolate, they may fail when used outside the domain of training data.

    But the part that worried Rafael Gómez-Bombarelli, the Jeffrey Cheah Career Development Professor in the MIT Department of Materials Science and Engineering, and graduate students Daniel Schwalbe-Koda and Aik Rui Tan was that establishing the limits of these machine learning (ML) models is tedious and labor-intensive. 

    This is particularly true for predicting ‘‘potential energy surfaces” (PES), or the map of a molecule’s energy in all its configurations. These surfaces encode the complexities of a molecule into flatlands, valleys, peaks, troughs, and ravines. The most stable configurations of a system are usually in the deep pits — quantum mechanical chasms from which atoms and molecules typically do not escape. 

    In a recent Nature Communications paper, the research team presented a way to demarcate the “safe zone” of a neural network by using “adversarial attacks.” Adversarial attacks have been studied for other classes of problems, such as image classification, but this is the first time that they are being used to sample molecular geometries in a PES. 

    “People have been using uncertainty for active learning for years in ML potentials. The key difference is that they need to run the full ML simulation and evaluate if the NN was reliable, and if it wasn’t, acquire more data, retrain and re-simulate. Meaning that it takes a long time to nail down the right model, and one has to run the ML simulation many times” explains Gómez-Bombarelli.

    The Gómez-Bombarelli lab at MIT works on a synergistic synthesis of first-principles simulation and machine learning that greatly speeds up this process. The actual simulations are run only for a small fraction of these molecules, and all those data are fed into a neural network that learns how to predict the same properties for the rest of the molecules. They have successfully demonstrated these methods for a growing class of novel materials that includes catalysts for producing hydrogen from water, cheaper polymer electrolytes for electric vehicles,  zeolites for molecular sieving, magnetic materials, and more. 

    The challenge, however, is that these neural networks are only as smart as the data they are trained on.  Considering the PES map, 99 percent of the data may fall into one pit, totally missing valleys that are of more interest. 

    Such wrong predictions can have disastrous consequences — think of a self-driving car that fails to identify a person crossing the street.

    One way to find out the uncertainty of a model is to run the same data through multiple versions of it. 

    For this project, the researchers had multiple neural networks predict the potential energy surface from the same data. Where the network is fairly sure of the prediction, the variation between the outputs of different networks is minimal and the surfaces largely converge. When the network is uncertain, the predictions of different models vary widely, producing a range of outputs, any of which could be the correct surface. 

    The spread in the predictions of a “committee of neural networks” is the “uncertainty” at that point. A good model should not just indicate the best prediction, but also indicates the uncertainty about each of these predictions. It’s like the neural network says “this property for material A will have a value of X and I’m highly confident about it.”

    This could have been an elegant solution but for the sheer scale of the combinatorial space. “Each simulation (which is ground feed for the neural network) may take from tens to thousands of CPU hours,” explains Schwalbe-Koda. For the results to be meaningful, multiple models must be run over a sufficient number of points in the PES, an extremely time-consuming process. 

    Instead, the new approach only samples data points from regions of low prediction confidence, corresponding to specific geometries of a molecule. These molecules are then stretched or deformed slightly so that the uncertainty of the neural network committee is maximized. Additional data are computed for these molecules through simulations and then added to the initial training pool. 

    The neural networks are trained again, and a new set of uncertainties are calculated. This process is repeated until the uncertainty associated with various points on the surface becomes well-defined and cannot be decreased any further. 

    Gómez-Bombarelli explains, “We aspire to have a model that is perfect in the regions we care about (i.e., the ones that the simulation will visit) without having had to run the full ML simulation, by making sure that we make it very good in high-likelihood regions where it isn’t.”

    The paper presents several examples of this approach, including predicting complex supramolecular interactions in zeolites. These materials are cavernous crystals that act as molecular sieves with high shape selectivity. They find applications in catalysis, gas separation, and ion exchange, among others.

    Because performing simulations of large zeolite structures is very costly, the researchers show how their method can provide significant savings in computational simulations. They used more than 15,000 examples to train a neural network to predict the potential energy surfaces for these systems. Despite the large cost required to generate the dataset, the final results are mediocre, with only around 80 percent of the neural network-based simulations being successful. To improve the performance of the model using traditional active learning methods, the researchers calculated an additional 5,000 data points, which improved the performance of the neural network potentials to 92 percent.

    However, when the adversarial approach is used to retrain the neural networks, the authors saw a performance jump to 97 percent using only 500 extra points. That’s a remarkable result, the researchers say, especially considering that each of these extra points takes hundreds of CPU hours. 

    This could be the most realistic method to probe the limits of models that researchers use to predict the behavior of materials and the progress of chemical reactions. More

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    Making the case for hydrogen in a zero-carbon economy

    As the United States races to achieve its goal of zero-carbon electricity generation by 2035, energy providers are swiftly ramping up renewable resources such as solar and wind. But because these technologies churn out electrons only when the sun shines and the wind blows, they need backup from other energy sources, especially during seasons of high electric demand. Currently, plants burning fossil fuels, primarily natural gas, fill in the gaps.

    “As we move to more and more renewable penetration, this intermittency will make a greater impact on the electric power system,” says Emre Gençer, a research scientist at the MIT Energy Initiative (MITEI). That’s because grid operators will increasingly resort to fossil-fuel-based “peaker” plants that compensate for the intermittency of the variable renewable energy (VRE) sources of sun and wind. “If we’re to achieve zero-carbon electricity, we must replace all greenhouse gas-emitting sources,” Gençer says.

    Low- and zero-carbon alternatives to greenhouse-gas emitting peaker plants are in development, such as arrays of lithium-ion batteries and hydrogen power generation. But each of these evolving technologies comes with its own set of advantages and constraints, and it has proven difficult to frame the debate about these options in a way that’s useful for policymakers, investors, and utilities engaged in the clean energy transition.

    Now, Gençer and Drake D. Hernandez SM ’21 have come up with a model that makes it possible to pin down the pros and cons of these peaker-plant alternatives with greater precision. Their hybrid technological and economic analysis, based on a detailed inventory of California’s power system, was published online last month in Applied Energy. While their work focuses on the most cost-effective solutions for replacing peaker power plants, it also contains insights intended to contribute to the larger conversation about transforming energy systems.

    “Our study’s essential takeaway is that hydrogen-fired power generation can be the more economical option when compared to lithium-ion batteries — even today, when the costs of hydrogen production, transmission, and storage are very high,” says Hernandez, who worked on the study while a graduate research assistant for MITEI. Adds Gençer, “If there is a place for hydrogen in the cases we analyzed, that suggests there is a promising role for hydrogen to play in the energy transition.”

    Adding up the costs

    California serves as a stellar paradigm for a swiftly shifting power system. The state draws more than 20 percent of its electricity from solar and approximately 7 percent from wind, with more VRE coming online rapidly. This means its peaker plants already play a pivotal role, coming online each evening when the sun goes down or when events such as heat waves drive up electricity use for days at a time.

    “We looked at all the peaker plants in California,” recounts Gençer. “We wanted to know the cost of electricity if we replaced them with hydrogen-fired turbines or with lithium-ion batteries.” The researchers used a core metric called the levelized cost of electricity (LCOE) as a way of comparing the costs of different technologies to each other. LCOE measures the average total cost of building and operating a particular energy-generating asset per unit of total electricity generated over the hypothetical lifetime of that asset.

    Selecting 2019 as their base study year, the team looked at the costs of running natural gas-fired peaker plants, which they defined as plants operating 15 percent of the year in response to gaps in intermittent renewable electricity. In addition, they determined the amount of carbon dioxide released by these plants and the expense of abating these emissions. Much of this information was publicly available.

    Coming up with prices for replacing peaker plants with massive arrays of lithium-ion batteries was also relatively straightforward: “There are no technical limitations to lithium-ion, so you can build as many as you want; but they are super expensive in terms of their footprint for energy storage and the mining required to manufacture them,” says Gençer.

    But then came the hard part: nailing down the costs of hydrogen-fired electricity generation. “The most difficult thing is finding cost assumptions for new technologies,” says Hernandez. “You can’t do this through a literature review, so we had many conversations with equipment manufacturers and plant operators.”

    The team considered two different forms of hydrogen fuel to replace natural gas, one produced through electrolyzer facilities that convert water and electricity into hydrogen, and another that reforms natural gas, yielding hydrogen and carbon waste that can be captured to reduce emissions. They also ran the numbers on retrofitting natural gas plants to burn hydrogen as opposed to building entirely new facilities. Their model includes identification of likely locations throughout the state and expenses involved in constructing these facilities.

    The researchers spent months compiling a giant dataset before setting out on the task of analysis. The results from their modeling were clear: “Hydrogen can be a more cost-effective alternative to lithium-ion batteries for peaking operations on a power grid,” says Hernandez. In addition, notes Gençer, “While certain technologies worked better in particular locations, we found that on average, reforming hydrogen rather than electrolytic hydrogen turned out to be the cheapest option for replacing peaker plants.”

    A tool for energy investors

    When he began this project, Gençer admits he “wasn’t hopeful” about hydrogen replacing natural gas in peaker plants. “It was kind of shocking to see in our different scenarios that there was a place for hydrogen.” That’s because the overall price tag for converting a fossil-fuel based plant to one based on hydrogen is very high, and such conversions likely won’t take place until more sectors of the economy embrace hydrogen, whether as a fuel for transportation or for varied manufacturing and industrial purposes.

    A nascent hydrogen production infrastructure does exist, mainly in the production of ammonia for fertilizer. But enormous investments will be necessary to expand this framework to meet grid-scale needs, driven by purposeful incentives. “With any of the climate solutions proposed today, we will need a carbon tax or carbon pricing; otherwise nobody will switch to new technologies,” says Gençer.

    The researchers believe studies like theirs could help key energy stakeholders make better-informed decisions. To that end, they have integrated their analysis into SESAME, a life cycle and techno-economic assessment tool for a range of energy systems that was developed by MIT researchers. Users can leverage this sophisticated modeling environment to compare costs of energy storage and emissions from different technologies, for instance, or to determine whether it is cost-efficient to replace a natural gas-powered plant with one powered by hydrogen.

    “As utilities, industry, and investors look to decarbonize and achieve zero-emissions targets, they have to weigh the costs of investing in low-carbon technologies today against the potential impacts of climate change moving forward,” says Hernandez, who is currently a senior associate in the energy practice at Charles River Associates. Hydrogen, he believes, will become increasingly cost-competitive as its production costs decline and markets expand.

    A study group member of MITEI’s soon-to-be published Future of Storage study, Gençer knows that hydrogen alone will not usher in a zero-carbon future. But, he says, “Our research shows we need to seriously consider hydrogen in the energy transition, start thinking about key areas where hydrogen should be used, and start making the massive investments necessary.”

    Funding for this research was provided by MITEI’s Low-Carbon Energy Centers and Future of Storage study. More

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    3 Questions: Peko Hosoi on the data-driven reasoning behind MIT’s Covid-19 policies for the fall

    As students, faculty, and staff prepare for a full return to the MIT campus in the weeks ahead, procedures for entering buildings, navigating classrooms and labs, and interacting with friends and colleagues will likely take some getting used to.

    The Institute recently reinforced its policies for indoor masking and has also continued to require regular testing for people who live, work, or study on campus — procedures that apply to both vaccinated and unvaccinated individuals. Vaccination is required for all students, faculty, and staff on campus unless a medical or religious exemption is granted.

    These and other policies adopted by MIT to control the spread of Covid-19 have been informed by modeling efforts from a volunteer group of MIT faculty, students, and postdocs. The collaboration, dubbed Isolat, was co-founded by Anette “Peko” Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean in the School of Engineering.

    The group, which is organized through MIT’s Institute for Data, Systems, and Society (IDSS), has run numerous models to show how measures such as mask wearing, testing, ventilation, and quarantining could affect Covid-19’s spread. These models have helped to shape MIT’s Covid-19 policies throughout the pandemic, including its procedures for returning to campus this fall.

    Hosoi spoke with MIT News about the data-backed reasoning behind some of these procedures, including indoor masking and regular testing, and how a “generous community” will help MIT safely weather the virus and its variants.

    Q: Take us through how you have been modeling Covid-19 and its variants, in regard to helping MIT shape its Covid policies. What’s the approach you’ve taken, and why?

    A: The approach we’re taking uses a simple counting exercise developed in IDSS to estimate the balance of testing, masking, and vaccination that is required to keep the virus in check. The underlying objective is to find infected people faster, on average, than they can infect others, which is captured in a simple algebraic expression. Our objective can be accomplished either by speeding up the rate of finding infected people (i.e. increasing testing frequency) or slowing down the rate of infection (i.e. increasing masking and vaccination) or by a combination of both. To give you a sense of the numbers, balances for different levels of testing are shown in the chart below for a vaccine efficacy of 67 percent and a contagious period of 18 days (which are the CDC’s latest parameters for the Delta variant).

    The vertical axis shows the now-famous reproduction number R0, i.e. the average number of people that one infected person will infect throughout the course of their illness. These R0 are averages for the population, and in specific circumstances the spreading could be more than that.

    Each blue line represents a different testing frequency: Below the line, the virus is controlled; above the line, it spreads. For example, the dotted blue line shows the boundary if we rely solely on vaccination with no testing. In that case, even if everyone is vaccinated, we can only control up to an R0 of about 3.  Unfortunately, the CDC places R0 of the Delta variant somewhere between 5 and 9, so vaccination alone is insufficient to control the spread. (As an aside, this also means that given the efficacy estimates for the current vaccines, herd immunity is not possible.)

    Next consider the dashed blue line, which represents the stability boundary if we test everyone once per week. If our vaccination rate is greater than about 90 percent, testing one time per week can control even the CDC’s most pessimistic estimate for the Delta variant’s R0.

    Q: In returning to campus over the next few weeks, indoor masking and regular testing are required of every MIT community member, even those who are vaccinated. What in your modeling has shown that each of these policies is necessary?

    A: Given that the chart above shows that vaccination and weekly testing are sufficient to control the virus, one should certainly ask “Why have we reinstated indoor masking?” The answer is related to the fact that, as a university, our population turns over once a year; every September we bring in a few thousand new people. Those people are coming from all over the world, and some of them may not have had the opportunity to get vaccinated yet. The good news is that MIT Medical has vaccines and will be administering them to any unvaccinated students as soon as they arrive; the bad news is that, as we all know, it takes three to five weeks for resistance to build up, depending on the vaccine. This means that we should think of August and September as a transition period during which the vaccination rates may fluctuate as new people arrive. 

    The other revelation that has informed our policies for September is the recent report from the CDC that infected vaccinated people carry roughly the same viral load as unvaccinated infected people. This suggests that vaccinated people — although they are highly unlikely to get seriously ill — are a consequential part of the transmission chain and can pass the virus along to others. So, in order to avoid giving the virus to people who are not yet fully vaccinated during the transition period, we all need to exercise a little extra care to give the newly vaccinated time for their immune systems to ramp up. 

    Q: As the fall progresses, what signs are you looking for that might shift decisions on masking and testing on campus?

    A: Eventually we will have to shift responsibility toward individuals rather than institutions, and allow people to make decisions about masks and testing based on their own risk tolerance. The success of the vaccines in suppressing severe illness will enable us to shift to a position in which our objective is not necessarily to control the spread of the virus, but rather to reduce the risk of serious outcomes to an acceptable level. There are many people who believe we need to make this adjustment and wean ourselves off pandemic living. They are right; we cannot continue like this forever. However, we have not played all our cards yet, and, in my opinion, we need to carefully consider what’s left in our hand before we abdicate institutional responsibility.

    The final ace we have to play is vaccinating kids. It is important to remember that we have many people in our community with kids who are too young to be vaccinated and, understandably, those parents do not want to bring Covid home to their children. Furthermore, our campus is not just a workplace; it is also home to thousands of people, some of whom have children living in our residences or attending an MIT childcare center. Given that context, and the high probability that a vaccine will be approved for children in the near future, it is my belief that our community has the empathy and fortitude to try to keep the virus in check until parents have the option to protect their children with vaccines. 

    Bearing in mind that children constitute an unprotected portion of our population, let me return to the original question and speculate on the fate of masks and testing in the fall. Regarding testing, the analysis suggests that we cannot give that up entirely if we would like to control the spread of the virus. Second, control of the virus is not the only benefit we get from testing. It also gives us situational awareness, serves as an early warning beacon, and provides information that individual members of the community can use as they make decisions about their own risk budget. Personally, I’ve been testing for a year now and I find it easy and reassuring. Honestly, it’s nice to know that I’m Covid-free before I see friends (outside!) or go home to my family.

    Regarding masks, there is always uncertainty around whether a new variant will arise or whether vaccine efficacy will fade, but, given the current parameters and our analysis, my hope is that we will be in a position to provide some relief on the mask mandate once the incoming members of our population have been fully vaccinated. I also suspect that whenever the mask mandate is lifted, masks are not likely to go away. There are certainly situations in which I will continue to wear a mask regardless of the mandate, and many in our community will continue to feel safer wearing masks even when they are not required.

    I believe that we are a generous community and that we will be willing to take precautions to help keep each other healthy. The students who were on campus last year did an outstanding job, and they have given me a tremendous amount of faith that we can be considerate and good to one another even in extremely trying times.

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    Last-mile routing research challenge awards $175,000 to three winning teams

    Routing is one of the most studied problems in operations research; even small improvements in routing efficiency can save companies money and result in energy savings and reduced environmental impacts. Now, three teams of researchers from universities around the world have received prize money totaling $175,000 for their innovative route optimization models.

    The three teams were the winners of the Amazon Last-Mile Routing Research Challenge, through which the MIT Center for Transportation & Logistics (MIT CTL) and Amazon engaged with a global community of researchers across a range of disciplines, from computer science to business operations to supply chain management, challenging them to build data-driven route optimization models leveraging massive historical route execution data.

    First announced in February, the research challenge attracted more than 2,000 participants from around the world. Two hundred twenty-nine researcher teams formed during the spring to independently develop solutions that incorporated driver know-how into route optimization models with the intent that they would outperform traditional optimization approaches. Out of the 48 teams whose models qualified for the final round of the challenge, three teams’ work stood out above the rest. Amazon provided real operational training data for the models and evaluated submissions, with technical support from MIT CTL scientists.

    In real life, drivers frequently deviate from planned and mathematically optimized route sequences. Drivers carry information about which roads are hard to navigate when traffic is bad, when and where they can easily find parking, which stops can be conveniently served together, and many other factors that existing optimization models simply don’t capture.

    Each model addressed the challenge data in a unique way. The methodological approaches chosen by the participants frequently combined traditional exact and heuristic optimization approaches with nontraditional machine learning methods. On the machine learning side, the most commonly adopted methods were different variants of artificial neural networks, as well as inverse reinforcement learning approaches.

    There were 45 submissions that reached the finalist phase, with team members hailing from 29 countries. Entrants spanned all levels of higher education from final-year undergraduate students to retired faculty. Entries were assessed in a double-blind review process so that the judges would not know what team was attached to each entry.

    The third-place prize of $25,000 was awarded to Okan Arslan and Rasit Abay. Okan is a professor at HEC Montréal, and Rasit is a doctoral student at the University of New South Wales in Australia. The runner-up prize at $50,000 was awarded to MIT’s own Xiaotong Guo, Qingyi Wang, and Baichuan Mo, all doctoral students. The top prize of $100,000 was awarded to Professor William Cook of the University of Waterloo in Canada, Professor Stephan Held of the University of Bonn in Germany, and Professor Emeritus Keld Helsgaun of Roskilde University in Denmark. Congratulations to all winners and contestants were held via webinar on July 30.

    Top-performing teams may be interviewed by Amazon for research roles in the company’s Last Mile organization. MIT CTL will publish and promote short technical papers written by all finalists and might invite top-performing teams to present at MIT. Further, a team led by Matthias Winkenbach, director of the MIT Megacity Logistics Lab, will guest-edit a special issue of Transportation Science, one of the most renowned academic journals in this field, featuring academic papers on topics related to the problem tackled by the research challenge. More

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    Helping companies optimize their websites and mobile apps

    Creating a good customer experience increasingly means creating a good digital experience. But metrics like pageviews and clicks offer limited insight into how much customers actually like a digital product.

    That’s the problem the digital optimization company Amplitude is solving. Amplitude gives companies a clearer picture into how users interact with their digital products to help them understand exactly which features to promote or improve.

    “It’s all about using product data to drive your business,” says Amplitude CEO Spenser Skates ’10, who co-founded the company with Curtis Liu ’10 and Stanford University graduate Jeffrey Wang. “Mobile apps and websites are really complex. The average app or website will have thousands of things you can do with it. The question is how you know which of those things are driving a great user experience and which parts are really frustrating for users.”

    Amplitude’s database can gather millions of details about how users behave inside an app or website and allow customers to explore that information without needing data science degrees.

    “It provides an interface for very easy, accessible ways of looking at your data, understanding your data, and asking questions of that data,” Skates says.

    Amplitude, which recently announced it will be going public, is already helping 23 of the 100 largest companies in the U.S. Customers include media companies like NBC, tech companies like Twitter, and retail companies like Walmart.

    “Our platform helps businesses understand how people are using their apps and websites so they can create better versions of their products,” Skates says. “It’s all about creating a really compelling product.”

    Learning entrepreneurship

    The founders say their years at MIT were among the best of their lives. Skates and Liu were undergraduates from 2006 to 2010. Skates majored in biological engineering while Liu majored in mathematics and electrical engineering and computer science. The two first met as opponents in MIT’s Battlecode competition, in which students use artificial intelligence algorithms to control teams of robots that compete in a strategy game against other teams. The following year they teamed up.

    “There are a lot of parallels between what you’re trying to do in Battlecode and what you end up having to do in the early stages of a startup,” Liu says. “You have limited resources, limited time, and you’re trying to accomplish a goal. What we found is trying a lot of different things, putting our ideas out there and testing them with real data, really helped us focus on the things that actually mattered. That method of iteration and continual improvement set the foundation for how we approach building products and startups.”

    Liu and Skates next participated in the MIT $100K Entrepreneurship Competition with an idea for a cloud-based music streaming service. After graduation, Skates began working in finance and Liu got a job at Google, but they continued pursuing startup ideas on the side, including a website that let alumni see where their classmates ended up and a marketplace for finding photographers.

    A year after graduation, the founders decided to quit their jobs and work on a startup full time. Skates moved into Liu’s apartment in San Francisco, setting up a mattress on the floor, and they began working on a project that became Sonalight, a voice recognition app. As part of the project, the founders built an internal system to understand where users got stuck in the app and what features were used the most.

    Despite getting over 100,000 downloads, the founders decided Sonalight was a little too early for its time and started thinking their analytics feature could be useful to other companies. They spoke with about 30 different product teams to learn more about what companies wanted from their digital analytics. Amplitude was officially founded in 2012.

    Amplitude gathers fine details about digital product usage, parsing out individual features and actions to give customers a better view of how their products are being used. Using the data in Amplitude’s intuitive, no-code interface, customers can make strategic decisions like whether to launch a feature or change a distribution channel.

    The platform is designed to ease the bottlenecks that arise when executives, product teams, salespeople, and marketers want to answer questions about customer experience or behavior but need the data science team to crunch the numbers for them.

    “It’s a very collaborative interface to encourage customers to work together to understand how users are engaging with their apps,” Skates says.

    Amplitude’s database also uses machine learning to segment users, predict user outcomes, and uncover novel correlations. Earlier this year, the company unveiled a service called Recommend that helps companies create personalized user experiences across their entire platform in minutes. The service goes beyond demographics to personalize customer experiences based on what users have done or seen before within the product.

    “We’re very conscious on the privacy front,” Skates says. “A lot of analytics companies will resell your data to third parties or use it for advertising purposes. We don’t do any of that. We’re only here to provide product insights to our customers. We’re not using data to track you across the web. Everyone expects Netflix to use the data on what you’ve watched before to recommend what to watch next. That’s effectively what we’re helping other companies do.”

    Optimizing digital experiences

    The meditation app Calm is on a mission to help users build habits that improve their mental wellness. Using Amplitude, the company learned that users most often use the app to get better sleep and reduce stress. The insights helped Calm’s team double down on content geared toward those goals, launching “sleep stories” to help users unwind at the end of each day and adding content around anxiety relief and relaxation. Sleep stories are now Calm’s most popular type of content, and Calm has grown rapidly to millions of people around the world.

    Calm’s story shows the power of letting user behavior drive product decisions. Amplitude has also helped the online fundraising site GoFundMe increase donations by showing users more compelling campaigns and the exercise bike company Peloton realize the importance of social features like leaderboards.

    Moving forward, the founders believe Amplitude’s platform will continue helping companies adapt to an increasingly digital world in which users expect more compelling, personalized experiences.

    “If you think about the online experience for companies today compared to 10 years ago, now [digital] is the main point of contact, whether you’re a media company streaming content, a retail company, or a finance company,” Skates says. “That’s only going to continue. That’s where we’re trying to help.” More

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    Smarter regulation of global shipping emissions could improve air quality and health outcomes

    Emissions from shipping activities around the world account for nearly 3 percent of total human-caused greenhouse gas emissions, and could increase by up to 50 percent by 2050, making them an important and often overlooked target for global climate mitigation. At the same time, shipping-related emissions of additional pollutants, particularly nitrogen and sulfur oxides, pose a significant threat to global health, as they degrade air quality enough to cause premature deaths.

    The main source of shipping emissions is the combustion of heavy fuel oil in large diesel engines, which disperses pollutants into the air over coastal areas. The nitrogen and sulfur oxides emitted from these engines contribute to the formation of PM2.5, airborne particulates with diameters of up to 2.5 micrometers that are linked to respiratory and cardiovascular diseases. Previous studies have estimated that PM2.5  from shipping emissions contribute to about 60,000 cardiopulmonary and lung cancer deaths each year, and that IMO 2020, an international policy that caps engine fuel sulfur content at 0.5 percent, could reduce PM2.5 concentrations enough to lower annual premature mortality by 34 percent.

    Global shipping emissions arise from both domestic (between ports in the same country) and international (between ports of different countries) shipping activities, and are governed by national and international policies, respectively. Consequently, effective mitigation of the air quality and health impacts of global shipping emissions will require that policymakers quantify the relative contributions of domestic and international shipping activities to these adverse impacts in an integrated global analysis.

    A new study in the journal Environmental Research Letters provides that kind of analysis for the first time. To that end, the study’s co-authors — researchers from MIT and the Hong Kong University of Science and Technology — implement a three-step process. First, they create global shipping emission inventories for domestic and international vessels based on ship activity records of the year 2015 from the Automatic Identification System (AIS). Second, they apply an atmospheric chemistry and transport model to this data to calculate PM2.5 concentrations generated by that year’s domestic and international shipping activities. Finally, they apply a model that estimates mortalities attributable to these pollutant concentrations.

    The researchers find that approximately 94,000 premature deaths were associated with PM2.5 exposure due to maritime shipping in 2015 — 83 percent international and 17 percent domestic. While international shipping accounted for the vast majority of the global health impact, some regions experienced significant health burdens from domestic shipping operations. This is especially true in East Asia: In China, 44 percent of shipping-related premature deaths were attributable to domestic shipping activities.

    “By comparing the health impacts from international and domestic shipping at the global level, our study could help inform decision-makers’ efforts to coordinate shipping emissions policies across multiple scales, and thereby reduce the air quality and health impacts of these emissions more effectively,” says Yiqi Zhang, a researcher at the Hong Kong University of Science and Technology who led the study as a visiting student supported by the MIT Joint Program on the Science and Policy of Global Change.

    In addition to estimating the air-quality and health impacts of domestic and international shipping, the researchers evaluate potential health outcomes under different shipping emissions-control policies that are either currently in effect or likely to be implemented in different regions in the near future.

    They estimate about 30,000 avoided deaths per year under a scenario consistent with IMO 2020, an international regulation limiting the sulfur content in shipping fuel oil to 0.5 percent — a finding that tracks with previous studies. Further strengthening regulations on sulfur content would yield only slight improvement; limiting sulfur content to 0.1 percent reduces annual shipping-attributable PM2.5-related premature deaths by an additional 5,000. In contrast, regulating nitrogen oxides instead, involving a Tier III NOx Standard would produce far greater benefits than a 0.1-percent sulfur cap, with 33,000 further avoided deaths.

    “Areas with high proportions of mortalities contributed by domestic shipping could effectively use domestic regulations to implement controls,” says study co-author Noelle Selin, a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, and a faculty affiliate of the MIT Joint Program. “For other regions where much damage comes from international vessels, further international cooperation is required to mitigate impacts.” More