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    Q&A: A fresh look at data science

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    As the leaders of a developing field, data scientists must often deal with a frustratingly slippery question: What is data science, precisely, and what is it good for?

    Alfred Spector is a visiting scholar in the MIT Department of Electrical Engineering and Computer Science (EECS), an influential developer of distributed computing systems and applications, and a successful tech executive with companies including IBM and Google. Along with three co-authors — Peter Norvig at Stanford University and Google, Chris Wiggins at Columbia University and The New York Times, and Jeannette M. Wing at Columbia — Spector recently published “Data Science in Context: Foundations, Challenges, Opportunities” (Cambridge University Press), which provides a broad, conversational overview of the wide-ranging field driving change in sectors ranging from health care to transportation to commerce to entertainment. 

    Here, Spector talks about data-driven life, what makes a good data scientist, and how his book came together during the height of the Covid-19 pandemic.

    Q: One of the most common buzzwords Americans hear is “data-driven,” but many might not know what that term is supposed to mean. Can you unpack it for us?

    A: Data-driven broadly refers to techniques or algorithms powered by data — they either provide insight or reach conclusions, say, a recommendation or a prediction. The algorithms power models which are increasingly woven into the fabric of science, commerce, and life, and they often provide excellent results. The list of their successes is really too long to even begin to list. However, one concern is that the proliferation of data makes it easy for us as students, scientists, or just members of the public to jump to erroneous conclusions. As just one example, our own confirmation biases make us prone to believing some data elements or insights “prove” something we already believe to be true. Additionally, we often tend to see causal relationships where the data only shows correlation. It might seem paradoxical, but data science makes critical reading and analysis of data all the more important.

    Q: What, to your mind, makes a good data scientist?

    A: [In talking to students and colleagues] I optimistically emphasize the power of data science and the importance of gaining the computational, statistical, and machine learning skills to apply it. But, I also remind students that we are obligated to solve problems well. In our book, Chris [Wiggins] paraphrases danah boyd, who says that a successful application of data science is not one that merely meets some technical goal, but one that actually improves lives. More specifically, I exhort practitioners to provide a real solution to problems, or else clearly identify what we are not solving so that people see the limitations of our work. We should be extremely clear so that we do not generate harmful results or lead others to erroneous conclusions. I also remind people that all of us, including scientists and engineers, are human and subject to the same human foibles as everyone else, such as various biases. 

    Q: You discuss Covid-19 in your book. While some short-range models for mortality were very accurate during the heart of the pandemic, you note the failure of long-range models to predict any of 2020’s four major geotemporal Covid waves in the United States. Do you feel Covid was a uniquely hard situation to model? 

    A: Covid was particularly difficult to predict over the long term because of many factors — the virus was changing, human behavior was changing, political entities changed their minds. Also, we didn’t have fine-grained mobility data (perhaps, for good reasons), and we lacked sufficient scientific understanding of the virus, particularly in the first year.

    I think there are many other domains which are similarly difficult. Our book teases out many reasons why data-driven models may not be applicable. Perhaps it’s too difficult to get or hold the necessary data. Perhaps the past doesn’t predict the future. If data models are being used in life-and-death situations, we may not be able to make them sufficiently dependable; this is particularly true as we’ve seen all the motivations that bad actors have to find vulnerabilities. So, as we continue to apply data science, we need to think through all the requirements we have, and the capability of the field to meet them. They often align, but not always. And, as data science seeks to solve problems into ever more important areas such as human health, education, transportation safety, etc., there will be many challenges.

    Q: Let’s talk about the power of good visualization. You mention the popular, early 2000’s Baby Name Voyager website as one that changed your view on the importance of data visualization. Tell us how that happened. 

    A: That website, recently reborn as the Name Grapher, had two characteristics that I thought were brilliant. First, it had a really natural interface, where you type the initial characters of a name and it shows a frequency graph of all the names beginning with those letters, and their popularity over time. Second, it’s so much better than a spreadsheet with 140 columns representing years and rows representing names, despite the fact it contains no extra information. It also provided instantaneous feedback with its display graph dynamically changing as you type. To me, this showed the power of a very simple transformation that is done correctly.

    Q: When you and your co-authors began planning “Data Science In Context,” what did you hope to offer?

    A: We portray present data science as a field that’s already had enormous benefits, that provides even more future opportunities, but one that requires equally enormous care in its use. Referencing the word “context” in the title, we explain that the proper use of data science must consider the specifics of the application, the laws and norms of the society in which the application is used, and even the time period of its deployment. And, importantly for an MIT audience, the practice of data science must go beyond just the data and the model to the careful consideration of an application’s objectives, its security, privacy, abuse, and resilience risks, and even the understandability it conveys to humans. Within this expansive notion of context, we finally explain that data scientists must also carefully consider ethical trade-offs and societal implications.

    Q: How did you keep focus throughout the process?

    A: Much like in open-source projects, I played both the coordinating author role and also the role of overall librarian of all the material, but we all made significant contributions. Chris Wiggins is very knowledgeable on the Belmont principles and applied ethics; he was the major contributor of those sections. Peter Norvig, as the coauthor of a bestselling AI textbook, was particularly involved in the sections on building models and causality. Jeannette Wing worked with me very closely on our seven-element Analysis Rubric and recognized that a checklist for data science practitioners would end up being one of our book’s most important contributions. 

    From a nuts-and-bolts perspective, we wrote the book during Covid, using one large shared Google doc with weekly video conferences. Amazingly enough, Chris, Jeannette, and I didn’t meet in person at all, and Peter and I met only once — sitting outdoors on a wooden bench on the Stanford campus.

    Q: That is an unusual way to write a book! Do you recommend it?

    A: It would be nice to have had more social interaction, but a shared document, at least with a coordinating author, worked pretty well for something up to this size. The benefit is that we always had a single, coherent textual base, not dissimilar to how a programming team works together.

    This is a condensed, edited version of a longer interview that originally appeared on the MIT EECS website. More

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      Companies use MIT research to identify and respond to supply chain risks

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      In February 2020, MIT professor David Simchi-Levi predicted the future. In an article in Harvard Business Review, he and his colleague warned that the new coronavirus outbreak would throttle supply chains and shutter tens of thousands of businesses across North America and Europe by mid-March.

      For Simchi-Levi, who had developed new models of supply chain resiliency and advised major companies on how to best shield themselves from supply chain woes, the signs of disruption were plain to see. Two years later, the professor of engineering systems at the MIT Schwarzman College of Computing and the Department of Civil and Environmental Engineering, and director of the MIT Data Science Lab has found a “flood of interest” from companies anxious to apply his Risk Exposure Index (REI) research to identify and respond to hidden risks in their own supply chains.

      His work on “stress tests” for critical supply chains and ways to guide global supply chain recovery were included in the 2022 Economic Report of the President presented to the U.S. Congress in April.

      It is rare that data science research can influence policy at the highest levels, Simchi-Levi says, but his models reflect something that business needs now: a new world of continuing global crisis, without relying on historical precedent.

      “What the last two years showed is that you cannot plan just based on what happened last year or the last two years,” Simchi-Levi says.

      He recalled the famous quote, sometimes attributed to hockey great Wayne Gretzsky, that good players don’t skate to where the puck is, but where the puck is going to be. “We are not focusing on the state of the supply chain right now, but what may happen six weeks from now, eight weeks from now, to prepare ourselves today to prevent the problems of the future.”

      Finding hidden risks

      At the heart of REI is a mathematical model of the supply chain that focuses on potential failures at different supply chain nodes — a flood at a supplier’s factory, or a shortage of raw materials at another factory, for instance. By calculating variables such as “time-to-recover” (TTR), which measures how long it will take a particular node to be back at full function, and time-to-survive (TTS), which identifies the maximum duration that the supply chain can match supply with demand after a disruption, the model focuses on the impact of disruption on the supply chain, rather than the cause of disruption.

      Even before the pandemic, catastrophic events such as the 2010 Iceland volcanic eruption and the 2011 Tohoku earthquake and tsunami in Japan were threatening these nodes. “For many years, companies from a variety of industries focused mostly on efficiency, cutting costs as much as possible, using strategies like outsourcing and offshoring,” Simchi-Levi says. “They were very successful doing this, but it has dramatically increased their exposure to risk.”

      Using their model, Simchi-Levi and colleagues began working with Ford Motor Company in 2013 to improve the company’s supply chain resiliency. The partnership uncovered some surprising hidden risks.

      To begin with, the researchers found out that Ford’s “strategic suppliers” — the nodes of the supply chain where the company spent large amount of money each year — had only moderate exposure to risk. Instead, the biggest risk “tended to come from tiny suppliers that provide Ford with components that cost about 10 cents,” says Simchi-Levi.

      The analysis also found that risky suppliers are everywhere across the globe. “There is this idea that if you just move suppliers closer to market, to demand, to North America or to Mexico, you increase the resiliency of your supply chain. That is not supported by our data,” he says.

      Rewards of resiliency

      By creating a virtual representation, or “digital twin,” of the Ford supply chain, the researchers were able to test out strategies at each node to see what would increase supply chain resiliency. Should the company invest in more warehouses to store a key component? Should it shift production of a component to another factory?

      Companies are sometimes reluctant to invest in supply chain resiliency, Simchi-Levi says, but the analysis isn’t just about risk. “It’s also going to help you identify savings opportunities. The company may be building a lot of misplaced, costly inventory, for instance, and our method helps them to identify these inefficiencies and cut costs.”

      Since working with Ford, Simchi-Levi and colleagues have collaborated with many other companies, including a partnership with Accenture, to scale the REI technology to a variety of industries including high-tech, industrial equipment, home improvement retailers, fashion retailers, and consumer packaged goods.

      Annette Clayton, the CEO of Schneider Electric North America and previously its chief supply chain officer, has worked with Simchi-Levi for 17 years. “When I first went to work for Schneider, I asked David and his team to help us look at resiliency and inventory positioning in order to make the best cost, delivery, flexibility, and speed trade-offs for the North American supply chain,” she says. “As the pandemic unfolded, the very learnings in supply chain resiliency we had worked on before became even more important and we partnered with David and his team again,”

      “We have used TTR and TTS to determine places where we need to develop and duplicate supplier capability, from raw materials to assembled parts. We increased inventories where our time-to-recover because of extended logistics times exceeded our time-to-survive,” Clayton adds. “We have used TTR and TTS to prioritize our workload in supplier development, procurement and expanding our own manufacturing capacity.”

      The REI approach can even be applied to an entire country’s economy, as the U.N. Office for Disaster Risk Reduction has done for developing countries such as Thailand in the wake of disastrous flooding in 2011.

      Simchi-Levi and colleagues have been motivated by the pandemic to enhance the REI model with new features. “Because we have started collaborating with more companies, we have realized some interesting, company-specific business constraints,” he says, which are leading to more efficient ways of calculating hidden risk. More

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      Study: With masking and distancing in place, NFL stadium openings in 2020 had no impact on local Covid-19 infections

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      As with most everything in the world, football looked very different in 2020. As the Covid-19 pandemic unfolded, many National Football League (NFL) games were played in empty stadiums, while other stadiums opened to fans at significantly reduced capacity, with strict safety protocols in place.

      At the time it was unclear what impact such large sporting events would have on Covid-19 case counts, particularly at a time when vaccination against the virus was not widely available.

      Now, MIT engineers have taken a look back at the NFL’s 2020 regular season and found that for this specific period during the pandemic, opening stadiums to fans while requiring face coverings, social distancing, and other measures had no impact on the number of Covid-19 infections in those stadiums’ local counties.

      As they write in a new paper appearing this week in the Proceedings of the National Academy of Sciences, “the benefits of providing a tightly controlled outdoor spectating environment — including masking and distancing requirements — counterbalanced the risks associated with opening.”

      The study concentrates on the NFL’s 2020 regular season (September 2020 to early January 2021), at a time when earlier strains of the virus dominated, before the rise of more transmissible Delta and Omicron variants. Nevertheless, the results may inform decisions on whether and how to hold large outdoor gatherings in the face of future public health crises.

      “These results show that the measures adopted by the NFL were effective in safely opening stadiums,” says study author Anette “Peko” Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering at MIT. “If case counts start to rise again, we know what to do: mask people, put them outside, and distance them from each other.”

      The study’s co-authors are members of MIT’s Institue for Data, Systems, and Society (IDSS), and include Bernardo García Bulle, Dennis Shen, and Devavrat Shah, the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science (EECS).

      Preseason patterns

      Last year a group led by the University of Southern Mississippi compared Covid-19 case counts in the counties of NFL stadiums that allowed fans in, versus those that did not. Their analysis showed that stadiums that opened to large numbers of fans led to “tangible increases” in the local county’s number of Covid-19 cases.

      But there are a number of factors in addition to a stadium’s opening that can affect case counts, including local policies, mandates, and attitudes. As the MIT team writes, “it is not at all obvious that one can attribute the differences in case spikes to the stadiums given the enormous number of confounding factors.”

      To truly isolate the effects of a stadium’s opening, one could imagine tracking Covid cases in a county with an open stadium through the 2020 season, then turning back the clock, closing the stadium, then tracking that same county’s Covid cases through the same season, all things being equal.

      “That’s the perfect experiment, with the exception that you would need a time machine,” Hosoi says.

      As it turns out, the next best thing is synthetic control — a statistical method that is used to determine the effect of an “intervention” (such as the opening of a stadium) compared with the exact same scenario without that intervention.

      In synthetic control, researchers use a weighted combination of groups to construct a “synthetic” version of an actual  scenario. In this case, the actual scenario is a county such as Dallas that hosts an open stadium. A synthetic version would be a county that looks similar to Dallas, only without a stadium. In the context of this study, a county that “looks” like Dallas has a similar preseason pattern of Covid-19 cases.

      To construct a synthetic Dallas, the researchers looked for surrounding counties without stadiums, that had similar Covid-19 trajectories leading up to the 2020 football season. They combined these counties in a way that best fit Dallas’ actual case trajectory. They then used data from the combined counties to calculate the number of Covid cases for this synthetic Dallas through the season, and compared these counts to the real Dallas.

      The team carried out this analysis for every “stadium county.” They determined a county to be a stadium county if more than 10 percent of a stadium’s fans came from that county, which the researchers estimated based on attendance data provided by the NFL.

      “Go outside”

      Of the stadiums included in the study, 13 were closed through the regular season, while 16 opened with reduced capacity and multiple pandemic requirements in place, such as required masking, distanced seating, mobile ticketing, and enhanced cleaning protocols.

      The researchers found the trajectory of infections in all stadium counties mirrored that of synthetic counties, showing that the number of infections would have been the same if the stadiums had remained closed. In other words, they found no evidence that NFL stadium openings led to any increase in local Covid case counts.

      To check that their method wasn’t missing any case spikes, they tested it on a known superspreader: the Sturgis Motorcycle Rally, which was held in August of 2020. The analysis successfully picked up an increase in cases in Meade, the host county, compared to a synthetic counterpart, in the two weeks following the rally.

      Surprisingly, the researchers found that several stadium counties’ case counts dipped slightly compared to their synthetic counterparts. In these counties — including Hamilton, Ohio, home of the Cincinnati Bengals — it appeared that opening the stadium to fans was tied to a dip in Covid-19 infections. Hosoi has a guess as to why:

      “These are football communities with dedicated fans. Rather than stay home alone, those fans may have gone to a sports bar or hosted indoor football gatherings if the stadium had not opened,” Hosoi proposes. “Opening the stadium under those circumstances would have been beneficial to the community because it makes people go outside.”

      The team’s analysis also revealed another connection: Counties with similar Covid trajectories also shared similar politics. To illustrate this point, the team mapped the county-wide temporal trajectories of Covid case counts in Ohio in 2020 and found them to be a strong predictor of the state’s 2020 electoral map.

      “That is not a coincidence,” Hosoi notes. “It tells us that local political leanings determined the temporal trajectory of the pandemic.”

      The team plans to apply their analysis to see how other factors may have influenced the pandemic.

      “Covid is a different beast [today],” she says. “Omicron is more transmissive, and more of the population is vaccinated. It’s possible we’d find something different if we ran this analysis on the upcoming season, and I think we probably should try.” More

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      Physics and the machine-learning “black box”

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      Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the “black box” problem, through a combination of data science and physics-based engineering.

      In class 2.C161 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

      “I wanted to take 2.C161 because machine-learning models are usually a “black box,” but this class taught us how to construct a system model that is informed by physics so we can peek inside,” explains Crystal Owens, a mechanical engineering graduate student who took the course in spring 2021.

      As chair of the Committee on the Strategic Integration of Data Science into Mechanical Engineering, Barbastathis has had many conversations with mechanical engineering students, researchers, and faculty to better understand the challenges and successes they’ve had using machine learning in their work.

      “One comment we heard frequently was that these colleagues can see the value of data science methods for problems they are facing in their mechanical engineering-centric research; yet they are lacking the tools to make the most out of it,” says Barbastathis. “Mechanical, civil, electrical, and other types of engineers want a fundamental understanding of data principles without having to convert themselves to being full-time data scientists or AI researchers.”

      Additionally, as mechanical engineering students move on from MIT to their careers, many will need to manage data scientists on their teams someday. Barbastathis hopes to set these students up for success with class 2.C161.

      Bridging MechE and the MIT Schwartzman College of Computing

      Class 2.C161 is part of the MIT Schwartzman College of Computing “Computing Core.” The goal of these classes is to connect data science and physics-based engineering disciplines, like mechanical engineering. Students take the course alongside 6.C402 (Modeling with Machine Learning: from Algorithms to Applications), taught by professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola.

      The two classes are taught concurrently during the semester, exposing students to both fundamentals in machine learning and domain-specific applications in mechanical engineering.

      In 2.C161, Barbastathis highlights how complementary physics-based engineering and data science are. Physical laws present a number of ambiguities and unknowns, ranging from temperature and humidity to electromagnetic forces. Data science can be used to predict these physical phenomena. Meanwhile, having an understanding of physical systems helps ensure the resulting output of an algorithm is accurate and explainable.

      “What’s needed is a deeper combined understanding of the associated physical phenomena and the principles of data science, machine learning in particular, to close the gap,” adds Barbastathis. “By combining data with physical principles, the new revolution in physics-based engineering is relatively immune to the “black box” problem facing other types of machine learning.”

      Equipped with a working knowledge of machine-learning topics covered in class 6.C402 and a deeper understanding of how to pair data science with physics, students are charged with developing a final project that solves for an actual physical system.

      Developing solutions for real-world physical systems

      For their final project, students in 2.C161 are asked to identify a real-world problem that requires data science to address the ambiguity inherent in physical systems. After obtaining all relevant data, students are asked to select a machine-learning method, implement their chosen solution, and present and critique the results.

      Topics this past semester ranged from weather forecasting to the flow of gas in combustion engines, with two student teams drawing inspiration from the ongoing Covid-19 pandemic.

      Owens and her teammates, fellow graduate students Arun Krishnadas and Joshua David John Rathinaraj, set out to develop a model for the Covid-19 vaccine rollout.

      “We developed a method of combining a neural network with a susceptible-infected-recovered (SIR) epidemiological model to create a physics-informed prediction system for the spread of Covid-19 after vaccinations started,” explains Owens.

      The team accounted for various unknowns including population mobility, weather, and political climate. This combined approach resulted in a prediction of Covid-19’s spread during the vaccine rollout that was more reliable than using either the SIR model or a neural network alone.

      Another team, including graduate student Yiwen Hu, developed a model to predict mutation rates in Covid-19, a topic that became all too pertinent as the delta variant began its global spread.

      “We used machine learning to predict the time-series-based mutation rate of Covid-19, and then incorporated that as an independent parameter into the prediction of pandemic dynamics to see if it could help us better predict the trend of the Covid-19 pandemic,” says Hu.

      Hu, who had previously conducted research into how vibrations on coronavirus protein spikes affect infection rates, hopes to apply the physics-based machine-learning approaches he learned in 2.C161 to his research on de novo protein design.

      Whatever the physical system students addressed in their final projects, Barbastathis was careful to stress one unifying goal: the need to assess ethical implications in data science. While more traditional computing methods like face or voice recognition have proven to be rife with ethical issues, there is an opportunity to combine physical systems with machine learning in a fair, ethical way.

      “We must ensure that collection and use of data are carried out equitably and inclusively, respecting the diversity in our society and avoiding well-known problems that computer scientists in the past have run into,” says Barbastathis.

      Barbastathis hopes that by encouraging mechanical engineering students to be both ethics-literate and well-versed in data science, they can move on to develop reliable, ethically sound solutions and predictions for physical-based engineering challenges. More

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      Studying learner engagement during the Covid-19 pandemic

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      While massive open online classes (MOOCs) have been a significant trend in higher education for many years now, they have gained a new level of attention during the Covid-19 pandemic. Open online courses became a critical resource for a wide audience of new learners during the first stages of the pandemic — including students whose academic programs had shifted online, teachers seeking online resources, and individuals suddenly facing lockdown or unemployment and looking to build new skills.

      Mary Ellen Wiltrout, director of online and blended learning initiatives and lecturer in digital learning in the Department of Biology, and Virginia “Katie” Blackwell, currently an MIT PhD student in biology, published a paper this summer in the European MOOC Stakeholder Summit (EMOOCs 2021) conference proceedings evaluating data for the online course 7.00x (Introduction to Biology). Their research objective was to better understand whether the shift to online learning that occurred during the pandemic led to increased learner engagement in the course.Blackwell participated in this research as part of the Bernard S. and Sophie G. Gould MIT Summer Research Program (MSRP) in Biology, during the uniquely remote MSRPx-Biology 2020 student cohort. She collaborated on the project while working toward her bachelor’s degree in biochemistry and molecular biology from the University of Texas at Dallas, and collaborated on the research while in Texas. She has since applied and been accepted into MIT’s PhD program in biology.

      “MSRP Biology was a transformative experience for me. I learned a lot about the nature of research and the MIT community in a very short period of time and loved every second of the program. Without MSRP, I would never have even considered applying to MIT for my PhD. After MSRP and working with Mary Ellen, MIT biology became my first-choice program and I felt like I had a shot at getting in,” says Blackwell.

      Play video

      Many MOOC platforms experienced increased website traffic in 2020, with 30 new MOOC-based degrees and more than 60 million new learners.

      “We find that the tremendous, lifelong learning opportunities that MOOCs provide are even more important and sought-after when traditional education is disrupted. During the pandemic, people had to be at home more often, and some faced unemployment requiring a career transition,” says Wiltrout.

      Wiltrout and Blackwell wanted to build a deeper understanding of learner profiles rather than looking exclusively at enrollments. They looked at all available data, including: enrollment demographics (i.e., country and “.edu” participants); proportion of learners engaged with videos, problems, and forums; number of individual engagement events with videos, problems, and forums; verification and performance; and the course “track” level — including auditing (for free) and verified (paying and receiving access to additional course content, including access to a comprehensive competency exam). They analyzed data in these areas from five runs of 7.00x in this study, including three pre-pandemic runs of April, July, and November 2019 and two pandemic runs of March and July 2020. 

      The March 2020 run had the same count of verified-track participants as all three pre-pandemic runs combined. The July 2020 run enrolled nearly as many verified-track participants as the March 2020 run. Wiltrout says that introductory biology content may have attracted great attention during the early days and months of the Covid-19 pandemic, as people may have had a new (or renewed) interest in learning about (or reviewing) viruses, RNA, the inner workings of cells, and more.

      Wiltrout and Blackwell found that the enrollment count for the March 2020 run of the course increased at almost triple the rate of the three pre-pandemic runs. During the early days of March 2020, the enrollment metrics appeared similar to enrollment metrics for the April 2019 run — both in rate and count — but the enrollment rate increased sharply around March 15, 2020. The July 2020 run began with more than twice as many learners already enrolled by the first day of the course, but continued with half the enrollment rate of the March 2020 course. In terms of learner demographics, during the pandemic, there was a higher proportion of learners with .edu addresses, indicating that MOOCs were often used by students enrolled in other schools. 

      Viewings of course videos increased at the beginning of the pandemic. During the March 2020 run, both verified-track and certified participants viewed far more unique videos during March 2020 than in the pre-pandemic runs of the course; even auditor-track learners — not aiming for certification — still viewed all videos offered. During the July 2020 run, however, both verified-track and certified participants viewed far fewer unique videos than during all prior runs. The proportion of participants who viewed at least one video decreased in the July 2020 run to 53 percent, from a mean of 64 percent in prior runs. Blackwell and Wiltrout say that this decrease — as well as the overall dip in participation in July 2020 — might be attributed to shifting circumstances for learners that allowed for less time to watch videos and participate in the course, as well as some fatigue from the extra screen time.

      The study found that 4.4 percent of March 2020 participants and 4.5 percent of July 2020 participants engaged through forum posting — which was 1.4 to 3.3 times higher than pre-pandemic proportions of forum posting. The increase in forum engagement may point to a desire for community engagement during a time when many were isolated and sheltering in place.

      “Through the day-to-day work of my research team and also through the engagement of the learners in 7.00x, we can see that there is great potential for meaningful connections in remote experiences,” says Wiltrout. “An increase in participation for an online course may not always remain at the same high level, in the long term, but overall, we’re continuing to see an increase in the number of MOOCs and other online programs offered by all universities and institutions, as well as an increase in online learners.” More

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      Exploring the human stories behind the data

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      Shaking in the back of a police cruiser, handcuffs digging into his wrists, Brian Williams was overwhelmed with fear. He had been pulled over, but before he was asked for his name, license, or registration, a police officer ordered him out of his car and into back of the police cruiser, saying into his radio, “Black male detained.” The officer’s explanation for these actions was: “for your safety and mine.”

      Williams walked away from the experience with two tickets, a pair of bruised wrists, and a desire to do everything in his power to prevent others from experiencing the utter powerlessness he had felt.

      Now an MIT senior majoring in biological engineering and minoring in Black studies, Williams has continued working to empower his community. Through experiences in and out of the classroom, he has leveraged his background in bioengineering to explore interests in public health and social justice, specifically looking at how the medical sector can uplift and support communities of color.

      Williams grew up in a close-knit family and community in Broward County, Florida, where he found comfort in the routine of Sunday church services, playing outside with friends, and cookouts on the weekends. Broward County was home to him — a home he felt deeply invested in and indebted to.

      “It takes a village. The Black community has invested a lot in me, and I have a lot to invest back in it,” he says.

      Williams initially focused on STEM subjects at MIT, but in his sophomore year, his interests in exploring data science and humanities research led him to an Undergraduate Research Opportunities Program (UROP) project in the Department of Political Science. Working with Professor Ariel White, he analyzed information on incarceration and voting rights, studied the behavior patterns of police officers, and screened 911 calls to identify correlations between how people described events to how the police responded to them.

      In the summer before his junior year, Williams also joined MIT’s Civic Data Design Lab, where he worked as a researcher for the Missing Data Project, which uses both journalism and data science to visualize statistics and humanize the people behind the numbers. As the project’s name suggests, there is often much to be learned from seeking out data that aren’t easily available. Using datasets and interviews describing how the pandemic affected Black communities, Williams and a team of researchers created a series called the Color of Covid, which told the stories behind the grim statistics on race and the pandemic.

      The following year, Williams undertook a research-and-development internship with the biopharmaceutical company Amgen in San Francisco, working on protein engineering to combat autoimmune diseases. Because this work was primarily in the lab, focusing on science-based applications, he saw it as an opportunity to ask himself: “Do I want to dedicate my life to this area of bioengineering?” He found the issue of social justice to be more compelling.

      At the same time, Williams was drawn toward tackling problems the local Black community was experiencing related to the pandemic. He found himself thinking deeply about how to educate the public, address disparities in case rates, and, above all, help people.

      Working through Amgen’s Black Employee Resource Group and its Diversity, Inclusion, and Belonging Team, Williams crafted a proposal, which the company adopted, for addressing Covid-19 vaccination misinformation in Black and Brown communities in San Mateo and San Francisco County. He paid special attention to how to frame vaccine hesitancy among members of these communities, understanding that a longstanding history of racism in scientific discovery and medicine led many Black and Brown people to distrust the entire medical industry.

      “Trying to meet people where they are is important,” Williams says.

      This experience reinforced the idea for Williams that he wanted to do everything in his power to uplift the Black community.

      “I think it’s only right that I go out and I shine bright because it’s not easy being Black. You know, you have to work twice as hard to get half as much,” he says.

      As the current political action co-chair of the MIT Black Students’ Union (BSU), Williams also works to inspire change on campus, promoting and participating in events that uplift the BSU. During his Amgen internship, he also organized the MIT Black History Month Takeover Series, which involved organizing eight events from February through the beginning of spring semester. These included promotions through social media and virtual meetings for students and faculty. For his leadership, he received the “We Are Family” award from the BSU executive board.

      Williams prioritizes community in everything he does, whether in the classroom, at a campus event, or spending time outside in local communities of color around Boston.

      “The things that really keep me going are the stories of other people,” says Williams, who is currently applying to a variety of postgraduate programs. After receiving MIT endorsement, he applied to the Rhodes and Marshall Fellowships; he also plans to apply to law school with a joint master’s degree in public health and policy.

      Ultimately, Williams hopes to bring his fight for racial justice to the policy level, looking at how a long, ongoing history of medical racism has led marginalized communities to mistrust current scientific endeavors. He wants to help bring about new legislation to fix old systems which disproportionately harm communities of color. He says he aims to be “an engineer of social solutions, one who reaches deep into their toolbox of social justice, pulling the levers of activism, advocacy, democracy, and legislation to radically change our world — to improve our social institutions at the root and liberate our communities.” While he understands this is a big feat, he sees his ambition as an asset.

      “I’m just another person with huge aspirations, and an understanding that you have to go get it if you want it,” he says. “You feel me? At the end of the day, this is just the beginning of my story. And I’m grateful to everyone in my life that’s helping me write it. Tap in.” More

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      in Data Management & Statistics

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

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      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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      in Data Management & Statistics

      “AI for Impact” lives up to its name

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      For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy.

      Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities. The app and related software eventually got spun out into the nonprofit PathCheck Foundation, which today engages with public health entities and is providing exposure notifications in Guam, Cyprus, Hawaii, Minnesota, Alabama, and Louisiana.

      The formation of Safe Paths demonstrates the special sense among MIT researchers that “we can launch something that can help people around the world,” notes Media Lab Associate Professor Ramesh Raskar, who teaches the class together with Media Lab Professor Alex “Sandy” Pentland and Media Lab Lecturer Joost Bonsen. “To have that kind of passion and ambition — but also the confidence that what you create here can actually be deployed globally — is kind of amazing.”

      AI for Impact, created by Pentland, began meeting two decades ago under the course name Development Ventures, and has nurtured multiple thriving businesses. Examples of class ventures that Pentland incubated or co-founded include Dimagi, Cogito, Ginger, Prosperia, and Sanergy.

      The aim-high challenge posed to each class is to come up with a business plan that touches a billion people, and it can’t all be in one country, Pentland explains. Not every class effort becomes a business, “but 20 percent to 30 percent of students start something, which is great for an entrepreneur class,” says Pentland.

      Opportunities for Impact

      The numbers behind Dimagi, for instance, are striking. Its core product CommCare has helped front-line health workers provide care for more than 400 million people in more than 130 countries around the world. When it comes to maternal and child care, Dimagi’s platform has registered one in every 110 pregnancies worldwide. This past year, several governments around the world deployed CommCare applications for Covid-19 response — from Sierra Leone and Somalia to New York and Colorado.

      Spinoffs like Cogito, Prosperia, and Ginger have likewise grown into highly successful companies. Cogito helps a million people a day gain access to the health care they need; Prosperia helps manage social support payments to 80 million people in Latin America; and Ginger handles mental health services for over 1 million people.

      The passion behind these and other class ventures points to a central idea of the class, Pentland notes: MIT students are often looking for ways to build entrepreneurial businesses that enable positive social change.

      During the spring 2021 class, for example, a number of promising student projects included tools to help residents of poor communities transition to owning their homes rather than renting, and to take better control of their community health.

      “It’s clear that the people who are graduating from here want to do something significant with their lives … they want to have an impact on their world,” Pentland says. “This class enables them to meet other people who are interested in doing the same thing, and offers them some help in starting a company to do it.”

      Many of the students who join the class come in with a broad set of interests. Guest lectures, case studies of other social entrepreneurship projects, and an introduction to a broad ecosystem of expertise and funding, then helps students to refine their general ideas into specific and viable projects.

      A path toward confronting a pandemic 

      Raskar began co-teaching the class in 2019, and brought a “Big AI” focus to the Development Ventures class, inspired by an AI for Impact team he had set up at his former employer, Facebook. “What I realized is that companies like Google or Facebook or Amazon actually have enough data about all of us that they can solve major problems in our society — climate, transportation, health, and so on,” he says. “This is something we should think about more seriously: how to use AI and data for positive social impact, while protecting privacy.”

      Early into the spring 2020 class, as students were beginning to consider their own projects, Raskar approached the class about the emerging coronavirus outbreak. Students like Kristen Vilcans recognized the urgency, and the opportunity. She and 10 other students joined forces to work on a project that would focus on Covid-19.

      “Students felt empowered to do something to help tackle the spread of this alarming new virus,” Raskar recalls. “They immediately began to develop data- and AI-based solutions to one of the most critical pieces of addressing a pandemic: halting the chain of infections. They created and launched one of the first digital contact tracing and exposure notification solutions in the U.S., developing an early alert system that engaged the public and protected privacy.” 

      Raskar looks back on the moment when a core group of students coalesced into a team. “It was very rare for a significant part of the class to just come together saying, ‘let’s do this, right away.’ It became as much a movement as a venture.”

      Group discussions soon began to center around an open-source, privacy-first digital set of tools for Covid-19 contact tracing. For the next two weeks, right up to the campus shutdown in March 2020, the team took over two adjacent conference rooms in the Media Lab, and started a Slack messaging channel devoted to the project. As the team members reached out to an ever-wider circle of friends, colleagues, and mentors, the number of participants grew to nearly 1,600 people, coming together virtually from all corners of the world.

      Kaushal Jain, a Harvard Business School student who had cross-registered for the spring 2020 class to get to know the MIT ecosystem, was also an early participant in Safe Paths. He wrote up an initial plan for the venture and began working with external organizations to figure out how to structure it into a nonprofit company. Jain eventually became the project’s lead for funding and partnerships.

      Vilcans, a graduate student in system design and management, served as Safe Paths’ communications lead through July 2020, while still working a part-time job at Draper Laboratory and taking classes.

      “There are these moments when you want to dive in, you want to contribute and you want to work nonstop,” she says, adding that the experience was also a wake-up call on how to manage burnout, and how to balance what you need as a person while contributing to a high-impact team. “That’s important to understand as a leader for the future.”

      MIT recognized Vilcan’s contributions later that year with the 2020 SDM Student Award for Leadership, Innovation, and Systems Thinking. 

      Jain, too, says the class gave him more than he could have expected.

      “I made strong friendships with like-minded people from very different backgrounds,” he says. “One key thing that I learned was to be flexible about the kind of work you want to do. Be open and see if there’s an opportunity, either through crisis or through something that you believe could really change a lot of things in the world. And then just go for it.” More

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      in Data Management & Statistics

      Study finds lockdowns effective at reducing travel in Sierra Leone

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      Throughout the Covid-19 pandemic, governments have used data on people’s movements to inform strategies for containing the spread of the virus. In Europe and the United States, for example, contact-tracing apps have used Bluetooth signals in smartphones to alert people when they’ve spent time near app users who have tested positive for Covid-19. 

      But how can governments make evidence-based decisions in countries where such fine-grained data isn’t available? In recent findings, MIT researchers, in collaboration with Sierra Leone’s government, use cell tower records in Sierra Leone to show that people were traveling less during lockdowns. “When the government implemented novel three-day lockdowns, there was a dual aim to reduce virus spread and also limit social impacts, like increased hunger or food insecurity,” says Professor Lily L. Tsai, MIT Governance Lab’s (MIT GOV/LAB) director and founder. “We wanted to know if shorter lockdowns would be successful.”   

      The research was conducted by MIT GOV/LAB and MIT’s Civic Data Design Lab (CDDL), in partnership with Sierra Leone’s Directorate for Science, Innovation and Technology (DSTI) and Africell, a wireless service provider. The findings will be published as a chapter in the book “Urban Informatics and Future Cities,” a selection of research submitted to the 2021 Computational Urban Planning and Urban Management conference. 

      A proxy for mobility: cell tower records

      Any time someone’s cellphone sends or receives a text, or makes or receives a call, the nearest cell tower is pinged. The tower collects some data (call-detail records, or CDRs), including the date and time of the event and the phone number. By tracking which towers a certain (anonymized) phone number pings, the researchers could approximately measure how much someone was moving around.  

      These measurements showed that, on average, people were traveling less during lockdowns than before lockdowns. Professor Sarah Williams, CDDL’s director, says the analysis also revealed frequently traveled routes, which “allow the government to develop region-specific lockdowns.” 

      While more fine-grained GPS data from smartphones paint a more accurate picture of movement, “there just isn’t a systematic effort in many developing countries to build the infrastructure to collect this data,” says Innocent Ndubuisi-Obi Jr., an MIT GOV/LAB research associate. “In many cases, the closest thing we can use as a proxy for mobility is CDR data.”

      Measuring the effectiveness of lockdowns

      Sierra Leone’s government imposed the three-day lockdown, which required people stay in their homes, in April 2020. A few days after the lockdown ended, a two-week inter-district travel ban began. “Analysis of aggregated CDRs was the quickest means to understanding mobility prior to and during lockdowns,” says Michala Mackay, DSTI’s director and chief operating officer. 

      The data MIT and DSTI received was anonymized — an essential part of ensuring the privacy of the individuals whose data was used. 

      Extracting meaning from the data, though, presented some challenges. Only about 75 percent of adults in Sierra Leone own cellphones, and people sometimes share phones. So the towers pinged by a specific phone might actually represent the movement of several people, and not everyone’s movement will be captured by cell towers. 

      Furthermore, some districts in Sierra Leone have significantly fewer towers than others. When the data were collected, Falaba, a rural district in the northeast, had only five towers, while over 100 towers were clustered in and around Freetown, the capital. In areas with very few towers, it’s harder to detect changes in how much people are traveling. 

      Since each district had a unique tower distribution, the researchers looked at each district separately, establishing a baseline for average distance traveled in each district before the lockdowns, then measuring how movement compared to this average during lockdowns. They found that travel to other districts declined in every district, by as much as 72 percent and by as little as 16 percent. Travel within districts also dropped in all but one district. 

      This map shows change in average distance traveled per trip to other districts in Sierra Leone in 2020.

      Image courtesy of the MIT GOV/LAB and CDDL.

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      Lockdowns have greater costs in poorer areas

      While movement did decline in all districts, the effect was less dramatic in poorer, more sparsely populated areas. This finding was to be expected; other studies have shown that poorer people often can’t afford to comply with lockdowns, since they can’t take time off work or need to travel to get food. Evidence showing how lockdowns are less effective in poorer areas highlights the importance of distributing resources to poorer areas during crises, which could both provide support during a particularly challenging time and make it less costly for people to comply with social distancing measures. 

      “In low-income communities that demonstrated moderate or low compliance, one of the most common reasons why people left their homes was to search for water,” says Mackay. “A policy takeaway was that lockdowns should only be implemented in extreme cases and for no longer than three days at a time.”

      Throughout the project, the researchers collaborated intimately with DSTI. “This meant government officials learned along with the MIT researchers and added crucial local knowledge,” says Williams. “We hope this model can be replicated elsewhere — especially during crises.” 

      The researchers will be developing an MITx course teaching government officials and MIT students how to collaboratively use CDR data during crises, with a focus on how to do the analysis in a way that protects people’s privacy.

      Ndubuisi-Obi Jr. also has led a training on CDR analysis for Sierra Leonean government officials and has written a guide on how policymakers can use CDRs safely and effectively. “Some of these data sets will help us answer really important policy questions, and we have to balance that with the privacy risks,” he says. More