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      Why big changes early in life can help later on

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      Imagine moving from state to state while growing up in the U.S., transferring between high schools, and eventually attending college out of state. The first two events might seem disruptive, and the third involves departing a local community. And yet, these things may be exactly what helps some people thrive later in life.

      That’s one implication of a newly published study about social networks co-authored by an MIT professor, which finds that so-called long ties — connections between people who otherwise lack any mutual contacts — are highly associated with greater economic success in life. Those long ties are fostered partly by turning points such as moving between states, and switching schools.

      The study, based on a large quantity of Facebook data, both illuminates how productive social networks are structured and identifies specific life events that significantly shape people’s networks.

      “People who have more long ties [on Facebook], and who have stronger long ties, have better economic indicators,” says Dean Eckles, an MIT professor and co-author of a new paper detailing the study’s findings.

      “Our hope is that the study provides better evidence of this really strong relationship, at the scale of the entire U.S,” Eckles says. “There hasn’t really been this sort of investigation into those types of disruptive life events.”

      The paper, “Long ties, disruptive life events, and economic prosperity,” appears in open-access form in Proceedings of the National Academy of Sciences. The authors are Eaman Jahani PhD ’21, a postdoc and lecturer at the University of California at Berkeley, who received his doctorate from MIT’s Institute for Data, Systems, and Society, and the Statistics and Data Science Center; Samuel P. Fraiberger, a data scientist at the World Bank; Michael Bailey, an economist and research scientist manager at Meta Platforms (which operates Facebook); and Eckles, an associate professor of marketing at MIT Sloan School of Management. Jahani, who worked at Meta when the study was conducted, performed the initial research, and the aggregate data analysis protected the privacy of individuals in compliance with regulations.

      On the move

      In recent decades, scholars have often analyzed social networks while building on a 1973 study by Stanford University’s Mark Granovetter, “The Strength of Weak Ties,” one of the 10 most-cited social science papers of all time. In it, Granovetter postulated that a network’s “weak ties”— the people you know less well — are vital. Your best friends may have networks quite similar to your own, but your “weak ties” provide additional connections useful for employment, and more. Granovetter also edited this current paper for PNAS.

      To conduct the study, the scholars mapped all reciprocal interactions among U.S.-based Facebook accounts from December 2020 to June 2021, to build a data-rich picture of social networks in action. The researchers maintain a distinction between “long” and “short” ties; in this definition, long ties have no other mutual connections at all, while short ties have some.

      Ultimately the scholars found that, when assessing everyone who has lived in the same state since 2012, those who had previously moved among U.S. states had 13 percent more long ties on Facebook than those who had not. Similarly, people who had switched high schools had 10 percent more long ties than people who had not.

      Facebook does not have income data for its users, so the scholars used a series of proxy measures to evaluate financial success. People with more long ties tend to live in higher-income areas, have more internet-connected devices, use more expensive mobile phones, and make more donations to charitable causes, compared to those who do not.

      Additionally, the research evaluates whether or not moving among states, or switching schools, is itself what causes people to have more long ties. After all, it could be the case that families who move more often have qualities that lead family members to be more proactive about forging ties with people.

      To examine this, the research team analyzed a subgroup of Facebook users who had switched high schools only when their first high school closed — meaning it was not their choice to change. Those people had 6 percent more long ties than those who had attended the same high schools but not been forced to switch; given this common pool of school attendees forced into divergent circumstances, the evidence suggests that making the school change itself “shapes the proclivity to connect with different communities,” as the scholars write in the paper. 

      “It’s a plausibly random nudge,” Eckles says, “and we find the people who were exposed to these high school closures end up with more long ties. I think that is one of the compelling elements pointing toward a causal story here.”

      Three types of events, same trend

      As the scholars acknowledge in the paper, there are some limitations to the study. Because it focuses on Facebook interactions, the research does not account for offline activities that may sustain social networks. It is also likely that economic success itself shapes people’s social networks, and not just that networks help shape success. Some people may have opportunities to maintain long ties, through professional work or travel, that others do not.

      On the other hand, the study does uncover long-term social network ties that had not been evaluated before, and, as the authors write,”having three different types of events — involving different processes by which people are selected into the disruption — pointing to the same conclusions makes for a more robust and notable pattern.”

      Other scholars in the field believe the study is a notable piece of research. In a commentary on the paper also published in PNAS, Michael Macy, a sociology professor at Cornell University, writes that “the authors demonstrate the importance of contributing to cumulative knowledge by confirming hypotheses derived from foundational theory while at the same time elaborating on what was previously known by digging deeper into the underlying causal mechanisms. In short, the paper is must reading not only for area specialists but for social scientists across the disciplines.”

      For his part, Eckles emphasizes that the researchers are releasing anonymized data from the study, so that other scholars can build on it, and develop additional insights about social network structure, while complying with all privacy regulations.

      “We’ve released [that] data and made it public, and we’re really happy to be doing that,” Eckles says. “We want to make as much of this as possible open to others. That’s one of the things that I’m hoping is part of the broader impact of the paper.”

      Jahani worked as a contractor at Meta Platforms, which operates Facebook, while conducting the research. Eckles has received past funding from Meta, as well as conference sponsorship, and previously worked there, before joining MIT.   More

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      in Data Management & Statistics

      Statistics, operations research, and better algorithms

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      In this day and age, many companies and institutions are not just data-driven, but data-intensive. Insurers, health providers, government agencies, and social media platforms are all heavily dependent on data-rich models and algorithms to identify the characteristics of the people who use them, and to nudge their behavior in various ways.

      That doesn’t mean organizations are always using optimal models, however. Determining efficient algorithms is a research area of its own — and one where Rahul Mazumder happens to be a leading expert.

      Mazumder, an associate professor in the MIT Sloan School of Management and an affiliate of the Operations Research Center, works both to expand the techniques of model-building and to refine models that apply to particular problems. His work pertains to a wealth of areas, including statistics and operations research, with applications in finance, health care, advertising, online recommendations, and more.

      “There is engineering involved, there is science involved, there is implementation involved, there is theory involved, it’s at the junction of various disciplines,” says Mazumder, who is also affiliated with the Center for Statistics and Data Science and the MIT-IBM Watson AI Lab.

      There is also a considerable amount of practical-minded judgment, logic, and common-sense decision-making at play, in order to bring the right techniques to bear on any individual task.

      “Statistics is about having data coming from a physical system, or computers, or humans, and you want to make sense of the data,” Mazumder says. “And you make sense of it by building models because that gives some pattern to a dataset. But of course, there is a lot of subjectivity in that. So, there is subjectivity in statistics, but also mathematical rigor.”

      Over roughly the last decade, Mazumder, often working with co-authors, has published about 40 peer-reviewed papers, won multiple academic awards, collaborated with major companies about their work, and helped advise graduate students. For his research and teaching, Mazumder was granted tenure by MIT last year.

      From deep roots to new tools

      Mazumder grew up in Kolkata, India, where his father was a professor at the Indian Statistical Institute and his mother was a schoolteacher. Mazumder received his undergraduate and master’s degrees from the Indian Statistical Institute as well, although without really focusing on the same areas as his father, whose work was in fluid mechanics.

      For his doctoral work, Mazumder attended Stanford University, where he earned his PhD in 2012. After a year as a postdoc at MIT’s Operations Research Center, he joined the faculty at Columbia University, then moved to MIT in 2015.

      While Mazumder’s work has many facets, his research portfolio does have notable central achievements. Mazumder has helped combine ideas from two branches of optimization to facilitate addressing computational problems in statistics. One of these branches, discrete optimization, uses discrete variables — integers — to find the best candidate among a finite set of options. This can relate to operational efficiency: What is the shortest route someone might take while making a designated set of stops? Convex optimization, on the other hand, encompasses an array of algorithms that can obtain the best solution for what Mazumder calls “nicely behaved” mathematical functions. They are typically applied to optimize continuous decisions in financial portfolio allocation and health care outcomes, among other things.

      In some recent papers, such as “Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms,” co-authored with Hussein Hazimeh and published in Operations Research in 2020, and in “Sparse regression at scale: branch-and-bound rooted in first-order optimization,” co-authored with Hazimeh and A. Saab and published in Mathematical Programming in 2022, Mazumder has found ways to combine ideas from the two branches.

      “The tools and techniques we are using are new for the class of statistical problems because we are combining different developments in convex optimization and exploring that within discrete optimization,” Mazumder says.

      As new as these tools are, however, Mazumder likes working on techniques that “have old roots,” as he puts it. The two types of optimization methods were considered less separate in the 1950s or 1960s, he says, then grew apart.

      “I like to go back and see how things developed,” Mazumder says. “If I look back in history at [older] papers, it’s actually very fascinating. One thing was developed, another was developed, another was developed kind of independently, and after a while you see connections across them. If I go back, I see some parallels. And that actually helps in my thought process.”

      Predictions and parsimony

      Mazumder’s work is often aimed at simplifying the model or algorithm being applied to a problem. In some instances, bigger models would require enormous amounts of processing power, so simpler methods can provide equally good results while using fewer resources. In other cases — ranging from the finance and tech firms Mazumder has sometimes collaborated with — simpler models may work better by having fewer moving parts.

      “There is a notion of parsimony involved,” Mazumder says. Genomic studies aim to find particularly influential genes; similarly, tech giants may benefit from simpler models of consumer behavior, not more complex ones, when they are recommending a movie to you.

      Very often, Mazumder says, modeling “is a very large-scale prediction problem. But we don’t think all the features or attributes are going to be important. A small collection is going to be important. Why? Because if you think about movies, there are not really 20,000 different movies; there are genres of movies. If you look at individual users, there are hundreds of millions of users, but really they are grouped together into cliques. Can you capture the parsimony in a model?”

      One part of his career that does not lend itself to parsimony, Mazumder feels, is crediting others. In conversation he emphasizes how grateful he is to his mentors in academia, and how much of his work is developed in concert with collaborators and, in particular, his students at MIT. 

      “I really, really like working with my students,” Mazumder says. “I perceive my students as my colleagues. Some of these problems, I thought they could not be solved, but then we just made it work. Of course, no method is perfect. But the fact we can use ideas from different areas in optimization with very deep roots, to address problems of core statistics and machine learning interest, is very exciting.”

      Teaching and doing research at MIT, Mazumder says, allows him to push forward on difficult problems — while also being pushed along by the interest and work of others around him.

      “MIT is a very vibrant community,” Mazumder says. “The thing I find really fascinating is, people here are very driven. They want to make a change in whatever area they are working in. And I also feel motivated to do this.” More

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      in Data Management & Statistics

      Q&A: Are far-reaching fires the new normal?

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      Where there’s smoke, there is fire. But with climate change, larger and longer-burning wildfires are sending smoke farther from their source, often to places that are unaccustomed to the exposure. That’s been the case this week, as smoke continues to drift south from massive wildfires in Canada, prompting warnings of hazardous air quality, and poor visibility in states across New England, the mid-Atlantic, and the Midwest.

      As wildfire season is just getting going, many may be wondering: Are the air-polluting effects of wildfires a new normal?

      MIT News spoke with Professor Colette Heald of the Department of Civil and Environmental Engineering and the Department of Earth, Atmospheric and Planetary Sciences, and Professor Noelle Selin of the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences. Heald specializes in atmospheric chemistry and has studied the climate and health effects associated with recent wildfires, while Selin works with atmospheric models to track air pollutants around the world, which she uses to inform policy decisions on mitigating  pollution and climate change. The researchers shared some of their insights on the immediate impacts of Canada’s current wildfires and what downwind regions may expect in the coming months, as the wildfire season stretches into summer.  

      Q: What role has climate change and human activity played in the wildfires we’ve seen so far this year?

      Heald: Unusually warm and dry conditions have dramatically increased fire susceptibility in Canada this year. Human-induced climate change makes such dry and warm conditions more likely. Smoke from fires in Alberta and Nova Scotia in May, and Quebec in early June, has led to some of the worst air quality conditions measured locally in Canada. This same smoke has been transported into the United States and degraded air quality here as well. Local officials have determined that ignitions have been associated with lightning strikes, but human activity has also played a role igniting some of the fires in Alberta.

      Q: What can we expect for the coming months in terms of the pattern of wildfires and their associated air pollution across the United States?

      Heald: The Government of Canada is projecting higher-than-normal fire activity throughout the 2023 fire season. Fire susceptibility will continue to respond to changing weather conditions, and whether the U.S. is impacted will depend on the winds and how air is transported across those regions. So far, the fire season in the United States has been below average, but fire risk is expected to increase modestly through the summer, so we may see local smoke influences as well.

      Q: How has air pollution from wildfires affected human health in the U.S. this year so far?

      Selin: The pollutant of most concern in wildfire smoke is fine particulate matter (PM2.5) – fine particles in the atmosphere that can be inhaled deep into the lungs, causing health damages. Exposure to PM2.5 causes respiratory and cardiovascular damage, including heart attacks and premature deaths. It can also cause symptoms like coughing and difficulty breathing. In New England this week, people have been breathing much higher concentrations of PM2.5 than usual. People who are particularly vulnerable to the effects are likely experiencing more severe impacts, such as older people and people with underlying conditions. But PM2.5 affects everyone. While the number and impact of wildfires varies from year to year, the associated air pollution from them generally lead to tens of thousands of premature deaths in the U.S. overall annually. There is also some evidence that PM2.5 from fires could be particularly damaging to health.

      While we in New England usually have relatively lower levels of pollution, it’s important also to note that some cities around the globe experience very high PM2.5 on a regular basis, not only from wildfires, but other sources such as power plants and industry. So, while we’re feeling the effects over the past few days, we should remember the broader importance of reducing PM2.5 levels overall for human health everywhere.

      Q: While firefighters battle fires directly this wildfire season, what can we do to reduce the effects of associated air pollution? And what can we do in the long-term, to prevent or reduce wildfire impacts?

      Selin: In the short term, protecting yourself from the impacts of PM2.5 is important. Limiting time outdoors, avoiding outdoor exercise, and wearing a high-quality mask are some strategies that can minimize exposure. Air filters can help reduce the concentrations of particles in indoor air. Taking measures to avoid exposure is particularly important for vulnerable groups. It’s also important to note that these strategies aren’t equally possible for everyone (for example, people who work outside) — stressing the importance of developing new strategies to address the underlying causes of increasing wildfires.

      Over the long term, mitigating climate change is important — because warm and dry conditions lead to wildfires, warming increases fire risk. Preventing the fires that are ignited by people or human activities can help.  Another way that damages can be mitigated in the longer term is by exploring land management strategies that could help manage fire intensity. More

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      Bringing the social and ethical responsibilities of computing to the forefront

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      There has been a remarkable surge in the use of algorithms and artificial intelligence to address a wide range of problems and challenges. While their adoption, particularly with the rise of AI, is reshaping nearly every industry sector, discipline, and area of research, such innovations often expose unexpected consequences that involve new norms, new expectations, and new rules and laws.

      To facilitate deeper understanding, the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative in the MIT Schwarzman College of Computing, recently brought together social scientists and humanists with computer scientists, engineers, and other computing faculty for an exploration of the ways in which the broad applicability of algorithms and AI has presented both opportunities and challenges in many aspects of society.

      “The very nature of our reality is changing. AI has the ability to do things that until recently were solely the realm of human intelligence — things that can challenge our understanding of what it means to be human,” remarked Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing, in his opening address at the inaugural SERC Symposium. “This poses philosophical, conceptual, and practical questions on a scale not experienced since the start of the Enlightenment. In the face of such profound change, we need new conceptual maps for navigating the change.”

      The symposium offered a glimpse into the vision and activities of SERC in both research and education. “We believe our responsibility with SERC is to educate and equip our students and enable our faculty to contribute to responsible technology development and deployment,” said Georgia Perakis, the William F. Pounds Professor of Management in the MIT Sloan School of Management, co-associate dean of SERC, and the lead organizer of the symposium. “We’re drawing from the many strengths and diversity of disciplines across MIT and beyond and bringing them together to gain multiple viewpoints.”

      Through a succession of panels and sessions, the symposium delved into a variety of topics related to the societal and ethical dimensions of computing. In addition, 37 undergraduate and graduate students from a range of majors, including urban studies and planning, political science, mathematics, biology, electrical engineering and computer science, and brain and cognitive sciences, participated in a poster session to exhibit their research in this space, covering such topics as quantum ethics, AI collusion in storage markets, computing waste, and empowering users on social platforms for better content credibility.

      Showcasing a diversity of work

      In three sessions devoted to themes of beneficent and fair computing, equitable and personalized health, and algorithms and humans, the SERC Symposium showcased work by 12 faculty members across these domains.

      One such project from a multidisciplinary team of archaeologists, architects, digital artists, and computational social scientists aimed to preserve endangered heritage sites in Afghanistan with digital twins. The project team produced highly detailed interrogable 3D models of the heritage sites, in addition to extended reality and virtual reality experiences, as learning resources for audiences that cannot access these sites.

      In a project for the United Network for Organ Sharing, researchers showed how they used applied analytics to optimize various facets of an organ allocation system in the United States that is currently undergoing a major overhaul in order to make it more efficient, equitable, and inclusive for different racial, age, and gender groups, among others.

      Another talk discussed an area that has not yet received adequate public attention: the broader implications for equity that biased sensor data holds for the next generation of models in computing and health care.

      A talk on bias in algorithms considered both human bias and algorithmic bias, and the potential for improving results by taking into account differences in the nature of the two kinds of bias.

      Other highlighted research included the interaction between online platforms and human psychology; a study on whether decision-makers make systemic prediction mistakes on the available information; and an illustration of how advanced analytics and computation can be leveraged to inform supply chain management, operations, and regulatory work in the food and pharmaceutical industries.

      Improving the algorithms of tomorrow

      “Algorithms are, without question, impacting every aspect of our lives,” said Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science, in kicking off a panel she moderated on the implications of data and algorithms.

      “Whether it’s in the context of social media, online commerce, automated tasks, and now a much wider range of creative interactions with the advent of generative AI tools and large language models, there’s little doubt that much more is to come,” Ozdaglar said. “While the promise is evident to all of us, there’s a lot to be concerned as well. This is very much time for imaginative thinking and careful deliberation to improve the algorithms of tomorrow.”

      Turning to the panel, Ozdaglar asked experts from computing, social science, and data science for insights on how to understand what is to come and shape it to enrich outcomes for the majority of humanity.

      Sarah Williams, associate professor of technology and urban planning at MIT, emphasized the critical importance of comprehending the process of how datasets are assembled, as data are the foundation for all models. She also stressed the need for research to address the potential implication of biases in algorithms that often find their way in through their creators and the data used in their development. “It’s up to us to think about our own ethical solutions to these problems,” she said. “Just as it’s important to progress with the technology, we need to start the field of looking at these questions of what biases are in the algorithms? What biases are in the data, or in that data’s journey?”

      Shifting focus to generative models and whether the development and use of these technologies should be regulated, the panelists — which also included MIT’s Srini Devadas, professor of electrical engineering and computer science, John Horton, professor of information technology, and Simon Johnson, professor of entrepreneurship — all concurred that regulating open-source algorithms, which are publicly accessible, would be difficult given that regulators are still catching up and struggling to even set guardrails for technology that is now 20 years old.

      Returning to the question of how to effectively regulate the use of these technologies, Johnson proposed a progressive corporate tax system as a potential solution. He recommends basing companies’ tax payments on their profits, especially for large corporations whose massive earnings go largely untaxed due to offshore banking. By doing so, Johnson said that this approach can serve as a regulatory mechanism that discourages companies from trying to “own the entire world” by imposing disincentives.

      The role of ethics in computing education

      As computing continues to advance with no signs of slowing down, it is critical to educate students to be intentional in the social impact of the technologies they will be developing and deploying into the world. But can one actually be taught such things? If so, how?

      Caspar Hare, professor of philosophy at MIT and co-associate dean of SERC, posed this looming question to faculty on a panel he moderated on the role of ethics in computing education. All experienced in teaching ethics and thinking about the social implications of computing, each panelist shared their perspective and approach.

      A strong advocate for the importance of learning from history, Eden Medina, associate professor of science, technology, and society at MIT, said that “often the way we frame computing is that everything is new. One of the things that I do in my teaching is look at how people have confronted these issues in the past and try to draw from them as a way to think about possible ways forward.” Medina regularly uses case studies in her classes and referred to a paper written by Yale University science historian Joanna Radin on the Pima Indian Diabetes Dataset that raised ethical issues on the history of that particular collection of data that many don’t consider as an example of how decisions around technology and data can grow out of very specific contexts.

      Milo Phillips-Brown, associate professor of philosophy at Oxford University, talked about the Ethical Computing Protocol that he co-created while he was a SERC postdoc at MIT. The protocol, a four-step approach to building technology responsibly, is designed to train computer science students to think in a better and more accurate way about the social implications of technology by breaking the process down into more manageable steps. “The basic approach that we take very much draws on the fields of value-sensitive design, responsible research and innovation, participatory design as guiding insights, and then is also fundamentally interdisciplinary,” he said.

      Fields such as biomedicine and law have an ethics ecosystem that distributes the function of ethical reasoning in these areas. Oversight and regulation are provided to guide front-line stakeholders and decision-makers when issues arise, as are training programs and access to interdisciplinary expertise that they can draw from. “In this space, we have none of that,” said John Basl, associate professor of philosophy at Northeastern University. “For current generations of computer scientists and other decision-makers, we’re actually making them do the ethical reasoning on their own.” Basl commented further that teaching core ethical reasoning skills across the curriculum, not just in philosophy classes, is essential, and that the goal shouldn’t be for every computer scientist be a professional ethicist, but for them to know enough of the landscape to be able to ask the right questions and seek out the relevant expertise and resources that exists.

      After the final session, interdisciplinary groups of faculty, students, and researchers engaged in animated discussions related to the issues covered throughout the day during a reception that marked the conclusion of the symposium. More

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      in Data Management & Statistics

      Celebrating the impact of IDSS

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      The “interdisciplinary approach” is something that has been lauded for decades for its ability to break down silos and create new integrated approaches to research.

      For Munther Dahleh, founding director of the MIT Institute for Data, Systems, and Society (IDSS), showing the community that data science and statistics can transcend individual disciplines and form a new holistic approach to addressing complex societal challenges has been crucial to the institute’s success.

      “From the very beginning, it was critical that we recognized the areas of data science, statistics, AI, and, in a way, computing, as transdisciplinary,” says Dahleh, who is the William A. Coolidge Professor in Electrical Engineering and Computer Science. “We made that point over and over — these are areas that embed in your field. It is not ours; this organization is here for everyone.”

      On April 14-15, researchers from across and beyond MIT joined together to celebrate the accomplishments and impact IDSS has had on research and education since its inception in 2015. Taking the place of IDSS’s annual statistics and data science conference SDSCon, the celebration also doubled as a way to recognize Dahleh for his work creating and executing the vision of IDSS as he prepares to step down from his director position this summer.

      In addition to talks and panels on statistics and computation, smart systems, automation and artificial intelligence, conference participants discussed issues ranging from climate change, health care, and misinformation. Nobel Prize winner and IDSS affiliate Professor Esther Duflo spoke on large scale immunization efforts, former MLK Visiting Professor Craig Watkins joined a panel on equity and justice in AI, and IDSS Associate Director Alberto Abadie discussed synthetic controls for policy evaluation. Other policy questions were explored through lightning talks, including those by students from the Technology and Policy Program (TPP) within IDSS.

      A place to call home

      The list of IDSS accomplishments over the last eight years is long and growing. From creating a home for 21st century statistics at MIT after other unsuccessful attempts, to creating a new PhD preparing the trilingual student who is an expert in data science and social science in the context of a domain, to playing a key role in determining an effective process for Covid testing in the early days of the pandemic, IDSS has left its mark on MIT. More recently, IDSS launched an initiative using big data to help effect structural and normative change toward racial equity, and will continue to explore societal challenges through the lenses of statistics, social science, and science and engineering.

      “I’m very proud of what we’ve done and of all the people who have contributed to this. The leadership team has been phenomenal in their commitment and their creativity,” Dahleh says. “I always say it doesn’t take one person, it takes the village to do what we have done, and I am very proud of that.”

      Prior to the institute’s formation, Dahleh and others at MIT were brought together to answer one key question: How would MIT prepare for the future of systems and data?

      “Data science is a complex area because in some ways it’s everywhere and it belongs to everyone, similar to statistics and AI,” Dahleh says “The most important part of creating an organization to support it was making it clear that it was an organization for everyone.” The response the team came back with was to build an Institute: a department that could cross all other departments and schools.

      While Dahleh and others on the committee were creating this blueprint for the future, the events that would lead early IDSS hires like Caroline Uhler to join the team were also beginning to take shape. Uhler, now an MIT professor of computer science and co-director of the Eric and Wendy Schmidt Center at the Broad Institute, was a panelist at the celebration discussing statistics and human health.

      In 2015, Uhler was a faculty member at the Institute of Science and Technology in Austria looking to move back to the U.S. “I was looking for positions in all different types of departments related to statistics, including electrical engineering and computer science, which were areas not related to my degree,” Uhler says. “What really got me to MIT was Munther’s vision for building a modern type of statistics, and the unique opportunity to be part of building what statistics should be moving forward.”

      The breadth of the Statistics and Data Science Center has given it a unique and a robust character that makes for an attractive collaborative environment at MIT. “A lot of IDSS’s impact has been in giving people like me a home,” Uhler adds. “By building an institute for statistics that is across all schools instead of housed within a single department, it has created a home for everyone who is interested in the field.”

      Filling the gap

      For Ali Jadbabaie, former IDSS associate director and another early IDSS hire, being in the right place at the right time landed him in the center of it all. A control theory expert and network scientist by training, Jadbabaie first came to MIT during a sabbatical from his position as a professor at the University of Pennsylvania.

      “My time at MIT coincided with the early discussions around forming IDSS and given my experience they asked me to stay and help with its creation,” Jadbabaie says. He is now head of the Department of Civil and Environmental Engineering at MIT, and he spoke at the celebration about a new MIT major in climate system science and engineering.

      A critical early accomplishment of IDSS was the creation of a doctoral program in social and engineering systems (SES), which has the goal of educating and fostering the success of a new type of PhD student, says Jadbabaie.

      “We realized we had this opportunity to educate a new type of PhD student who was conversant in the math of information sciences and statistics in addition to an understanding of a domain — infrastructures, climate, political polarization — in which problems arise,” he says. “This program would provide training in statistics and data science, the math of information sciences and a branch of social science that is relevant to their domain.”

      “SES has been filling a gap,” adds Jadbabaie. “We wanted to bring quantitative reasoning to areas in social sciences, particularly as they interact with complex engineering systems.”

      “My first year at MIT really broadened my horizon in terms of what was available and exciting,” says Manxi Wu, a member of the first cohort of students in the SES program after starting out in the Master of Science in Transportation (MST) program. “My advisor introduced me to a number of interesting topics at the intersection of game theory, economics, and engineering systems, and in my second year I realized my interest was really about the societal scale systems, with transportation as my go-to application area when I think about how to make an impact in the real world.”

      Wu, now an assistant professor in the School of Operations Research and Information Engineering at Cornell, was a panelist at the Celebration’s session on smart infrastructure systems. She says that the beauty of the SES program lies in its ability to create a common ground between groups of students and researchers who all have different applications interests but share an eagerness to sharpen their technical skills.

      “While we may be working on very different application areas, the core methodologies, such as mathematical tools for data science and probability optimization, create a common language,” Wu says. “We are all capable of speaking the technical language, and our diversified interests give us even more to talk about.”

      In addition to the PhD program, IDSS has helped bring quality MIT programming to people around the globe with its MicroMasters Program in Statistics and Data Science (SDS), which recently celebrated the certification of over 1,000 learners. The MicroMasters is just one offering in the newly-minted IDSSx, a collection of online learning opportunities for learners at different skill levels and interests.

      “The impact of branding what MIT-IDSS does across the globe has been great,” Dahleh says. “In addition, we’ve created smaller online programs for continued education in data science and machine learning, which I think is also critical in educating the community at large.”

      Hopes for the future

      Through all of its accomplishments, the core mission of IDSS has never changed.

      “The belief was always to create an institute focused on how data science can be used to solve pressing societal problems,” Dahleh says. “The organizational structure of IDSS as an MIT Institute has enabled it to promote data and systems as a transdiciplinary area that embeds in every domain to support its mission. This reverse ownership structure will continue to strengthen the presence of IDSS in MIT and will make it an essential unit within the Schwarzman College of Computing.”

      As Dahleh prepares to step down from his role, and Professor Martin Wainwright gets ready to fill his (very big) shoes as director, Dahleh’s colleagues say the real key to the success of IDSS all started with his passion and vision.

      “Creating a new academic unit within MIT is actually next to impossible,” Jadbabaie says. “It requires structural changes, as well as someone who has a strong understanding of multiple areas, who knows how to get people to work together collectively, and who has a mission.”

      “The most important thing is that he was inclusive,” he adds. “He didn’t try to create a gate around it and say these people are in and these people are not. I don’t think this would have ever happened without Munther at the helm.” More

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      Exploring new methods for increasing safety and reliability of autonomous vehicles

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      When we think of getting on the road in our cars, our first thoughts may not be that fellow drivers are particularly safe or careful — but human drivers are more reliable than one may expect. For each fatal car crash in the United States, motor vehicles log a whopping hundred million miles on the road.

      Human reliability also plays a role in how autonomous vehicles are integrated in the traffic system, especially around safety considerations. Human drivers continue to surpass autonomous vehicles in their ability to make quick decisions and perceive complex environments: Autonomous vehicles are known to struggle with seemingly common tasks, such as taking on- or off-ramps, or turning left in the face of oncoming traffic. Despite these enormous challenges, embracing autonomous vehicles in the future could yield great benefits, like clearing congested highways; enhancing freedom and mobility for non-drivers; and boosting driving efficiency, an important piece in fighting climate change.

      MIT engineer Cathy Wu envisions ways that autonomous vehicles could be deployed with their current shortcomings, without experiencing a dip in safety. “I started thinking more about the bottlenecks. It’s very clear that the main barrier to deployment of autonomous vehicles is safety and reliability,” Wu says.

      One path forward may be to introduce a hybrid system, in which autonomous vehicles handle easier scenarios on their own, like cruising on the highway, while transferring more complicated maneuvers to remote human operators. Wu, who is a member of the Laboratory for Information and Decision Systems (LIDS), a Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering (CEE) and a member of the MIT Institute for Data, Systems, and Society (IDSS), likens this approach to air traffic controllers on the ground directing commercial aircraft.

      In a paper published April 12 in IEEE Transactions on Robotics, Wu and co-authors Cameron Hickert and Sirui Li (both graduate students at LIDS) introduced a framework for how remote human supervision could be scaled to make a hybrid system efficient without compromising passenger safety. They noted that if autonomous vehicles were able to coordinate with each other on the road, they could reduce the number of moments in which humans needed to intervene.

      Humans and cars: finding a balance that’s just right

      For the project, Wu, Hickert, and Li sought to tackle a maneuver that autonomous vehicles often struggle to complete. They decided to focus on merging, specifically when vehicles use an on-ramp to enter a highway. In real life, merging cars must accelerate or slow down in order to avoid crashing into cars already on the road. In this scenario, if an autonomous vehicle was about to merge into traffic, remote human supervisors could momentarily take control of the vehicle to ensure a safe merge. In order to evaluate the efficiency of such a system, particularly while guaranteeing safety, the team specified the maximum amount of time each human supervisor would be expected to spend on a single merge. They were interested in understanding whether a small number of remote human supervisors could successfully manage a larger group of autonomous vehicles, and the extent to which this human-to-car ratio could be improved while still safely covering every merge.

      With more autonomous vehicles in use, one might assume a need for more remote supervisors. But in scenarios where autonomous vehicles coordinated with each other, the team found that cars could significantly reduce the number of times humans needed to step in. For example, a coordinating autonomous vehicle already on a highway could adjust its speed to make room for a merging car, eliminating a risky merging situation altogether.

      The team substantiated the potential to safely scale remote supervision in two theorems. First, using a mathematical framework known as queuing theory, the researchers formulated an expression to capture the probability of a given number of supervisors failing to handle all merges pooled together from multiple cars. This way, the researchers were able to assess how many remote supervisors would be needed in order to cover every potential merge conflict, depending on the number of autonomous vehicles in use. The researchers derived a second theorem to quantify the influence of cooperative autonomous vehicles on surrounding traffic for boosting reliability, to assist cars attempting to merge.

      When the team modeled a scenario in which 30 percent of cars on the road were cooperative autonomous vehicles, they estimated that a ratio of one human supervisor to every 47 autonomous vehicles could cover 99.9999 percent of merging cases. But this level of coverage drops below 99 percent, an unacceptable range, in scenarios where autonomous vehicles did not cooperate with each other.

      “If vehicles were to coordinate and basically prevent the need for supervision, that’s actually the best way to improve reliability,” Wu says.

      Cruising toward the future

      The team decided to focus on merging not only because it’s a challenge for autonomous vehicles, but also because it’s a well-defined task associated with a less-daunting scenario: driving on the highway. About half of the total miles traveled in the United States occur on interstates and other freeways. Since highways allow higher speeds than city roads, Wu says, “If you can fully automate highway driving … you give people back about a third of their driving time.”

      If it became feasible for autonomous vehicles to cruise unsupervised for most highway driving, the challenge of safely navigating complex or unexpected moments would remain. For instance, “you [would] need to be able to handle the start and end of the highway driving,” Wu says. You would also need to be able to manage times when passengers zone out or fall asleep, making them unable to quickly take over controls should it be needed. But if remote human supervisors could guide autonomous vehicles at key moments, passengers may never have to touch the wheel. Besides merging, other challenging situations on the highway include changing lanes and overtaking slower cars on the road.

      Although remote supervision and coordinated autonomous vehicles are hypotheticals for high-speed operations, and not currently in use, Wu hopes that thinking about these topics can encourage growth in the field.

      “This gives us some more confidence that the autonomous driving experience can happen,” Wu says. “I think we need to be more creative about what we mean by ‘autonomous vehicles.’ We want to give people back their time — safely. We want the benefits, we don’t strictly want something that drives autonomously.” More

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      Architectural heritage like you haven’t seen it before

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      The shrine of Khwaja Abu Nasr Parsa is a spectacular mosque in Balkh, Afghanistan. Also known as the “Green Mosque” due to the brilliant color of its tiled and painted dome, the intricately decorated building dates to the 16th century.

      If it were more accessible, the Green Mosque would attract many visitors. But Balkh is located in northern Afghanistan, roughly 50 miles from the border with Uzbekistan, and few outsiders will ever reach it. Still, anyone can now get a vivid sense of the mosque thanks to MIT’s new “Ways of Seeing” project, an innovative form of historic preservation.

      Play video

      PHD student Nikolaos Vlavianos created the following Extended Reality sequences for the “Ways of Seeing” project.

      “Ways of Seeing” uses multiple modes of imagery to produce a rich visual record of four historic building sites in Afghanistan — including colorful 3D still images, virtual reality imagery that takes viewers around and in some cases inside the structures, and exquisite hand-drawn architectural renderings of the buildings. The project’s imagery will be made available for viewing through the MIT Libraries by the end of June, with open access for the public. A subset of curated project materials will also be available through Archnet, an open access resource on the built environment of Muslim societies, which is a collaboration between the Aga Khan Documentation Center of the MIT Libraries and the Aga Khan Trust for Culture.

      “After the U.S. withdrawal from Afghanistan in August 2021, Associate Provost Richard Lester convened a set of MIT faculty in a working group to think of what we as a community of scholars could be doing that would be meaningful to people in Afghanistan at this point in time,” says Fotini Christia, an MIT political science professor who led the project. “‘Ways of Seeing’ is a project that I conceived after discussions with that group of colleagues and which is truly in the MIT tradition: It combines field data, technology, and art to protect heritage and serve the world.”

      Christia, the Ford International Professor of the Social Sciences and director of the Sociotechnical Systems Research Center at the MIT Schwarzman College of Computing, has worked extensively in Afghanistan conducting field research about civil society. She viewed this project as a unique opportunity to construct a detailed, accessible record of remarkable heritage sites — through sophisticated digital elements as well as finely wrought ink drawings.

      “The idea is these drawings would inspire interest and pride in this heritage, a kind of amazement and motivation to preserve this for as long as humanly possible,” says Jelena Pejkovic MArch ’06, a practicing architect who made the large-scale renderings by hand over a period of months.

      Pejkovic adds: “These drawings are extremely time-consuming, and for me this is part of the motivation. They ask you to slow down and pay attention. What can you take in from all this material that we have collected? How do you take time to look, to interpret, to understand what is in front of you?”

      The project’s “digital transformation strategy” was led by Nikolaos Vlavianos, a PhD candidate in the Department of Architecture’s Design and Computation group. The group uses cutting-edge technologies and drones to make three-dimensional digital reconstructions of large-scale architectural sites and create immersive experiences in extended reality (XR). Vlavianos also conducts studies of the psychological and physiological responses of humans experiencing such spaces in XR and in person. 

      “I regard this project as an effort toward a broader architectural metaverse consisting of immersive experiences in XR of physical spaces around the world that are difficult or impossible to access due to political, social, and even cultural constraints,” says Vlavianos. “These spaces in the metaverse are information hubs promoting an embodied experiential approach of living, sensing, seeing, hearing, and touching.”

      Nasser Rabbat, the Aga Khan Professor and director of the Aga Khan Program for Islamic Architecture at MIT, also offered advice and guidance on the early stages of the project.

      The project — formally titled “Ways of Seeing: Documenting Endangered Built Heritage in Afghanistan” — encompasses imaging of four quite varied historical sites in Afghanistan.

      These are the Green Mosque in Balkh; the Parwan Stupa, a Buddhist dome south of Kabul; the tomb of Gawhar Saad, in Herat, in honor of the queen of the emperor of the Timurid, who was herself a highly influential figure in the 14th and 15th centuries; and the Minaret of Jam, a remarkable 200-foot tall tower dating to the 12th century, next to the Hari River in a distant spot in western Afghanistan.

      The sites thus encompass multiple religions and a diversity of building types. Many are in remote locations within Afghanistan that cannot readily be accessed by visitors — including scholars.

      “Ways of Seeing” is supported by a Mellon Faculty Grant from the MIT Center for Art, Science, and Technology (CAST), and by faculty funding from the MIT School of Humanities, Arts, and Social Sciences (SHASS). It is co-presented with the Institute for Data, Systems, and Society (IDSS), the Sociotechnical Systems Research Center (SSRC) at the MIT Schwarzman College of Computing, the MIT Department of Political Science, and SHASS.

      Two students from Wellesley College participating in MIT’s Undergraduate Research Opportunities Program (UROP), juniors Meng Lu and Muzi Fang, also worked on the project under the guidance of Vlavianos to create a video game for children involving the Gawhar Saad heritage site. 

      To generate the imagery, the MIT team worked with an Afghan digital production team that was on the ground in the country; they went to the four sites and took thousands of pictures, having been trained remotely by Vlavianos to perform a 3D scanning aerial operation. They were led by Shafic Gawhari, the managing director for Afghanistan at the Moby Group, an international media enterprise; others involved were Mohammad Jan Kamal, Nazifullah Benaam, Warekzai Ghayoor, Rahm Ali Mohebzada, Mohammad Harif Ghobar, and Abdul Musawer Anwari.

      The journalists documented the sites by collecting 15,000 to 30,000 images, while Vlavianos computationally generated point clouds and mesh geometry with detailed texture mapping. The outcome of those models consisted of still images,  immersive experiences in XR, and data for Pejkovic.  

      “‘Ways of Seeing’ proposes a hybrid model of remote data collection,” says Vlavianos, who in his time at MIT has also led similar projects at Machu Picchu in Peru, and the Simonos Petra monastery at Mount Athos, Greece. To produce similar imagery even more easily, he says, “The next step — which I am working on — is to utilize autonomous drones deployed simultaneously in various locations on the world for rapid production and advanced neural network algorithms to generate models from lower number of images.”  

      In the future, Vlavianos envisions documenting and reconstructing other sites around the world using crowdsourcing data, historical images, satellite imagery, or even by having local communities learn XR techniques. 

      Pejkovic produced her drawings based on the digital models assembled by Vlavianos, carefully using a traditional rendering technique in which she would first outline the measurements of each structure, at scale, and then gradually ink in the drawings to give the buildings texture. The inking technique she used is based on VERNADOC, a method of documenting vernacular architecture developed by the Finnish architect Markku Mattila.

      “I wanted to rediscover the most traditional possible kind of documentation — measuring directly by hand, and drawing by hand,” says Pejkovic. She has been active in conservation of cultural heritage for over 10 years.

      The first time Pejkovic ever saw this type of hand-drawn renderings in person, she recalls thinking, “This is not possible, a human being cannot make drawings like this.” However, she wryly adds, “You know the motto at MIT is ‘mens et manus,’ mind and hand.” And so she embarked on hand drawing these renderings herself, at a large scale — her image of the Minaret of Jam has been printed in a crisp 8-foot version by the MIT team.

      “The ultimate intent of this project has been to make all these outputs, which are co-owned with the Afghans who carried out the data collection on the ground, available to Afghan refugees displaced around the world but also accessible to anyone keen to witness them,” Christia says. “The digital twins [representations] of these sites are also meant to work as repositories of information for any future preservation efforts. This model can be replicated and scaled for other heritage sites at risk from wars, environmental disaster, or cultural appropriation.” More

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      A better way to study ocean currents

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      To study ocean currents, scientists release GPS-tagged buoys in the ocean and record their velocities to reconstruct the currents that transport them. These buoy data are also used to identify “divergences,” which are areas where water rises up from below the surface or sinks beneath it.

      By accurately predicting currents and pinpointing divergences, scientists can more precisely forecast the weather, approximate how oil will spread after a spill, or measure energy transfer in the ocean. A new model that incorporates machine learning makes more accurate predictions than conventional models do, a new study reports.

      A multidisciplinary research team including computer scientists at MIT and oceanographers has found that a standard statistical model typically used on buoy data can struggle to accurately reconstruct currents or identify divergences because it makes unrealistic assumptions about the behavior of water.

      The researchers developed a new model that incorporates knowledge from fluid dynamics to better reflect the physics at work in ocean currents. They show that their method, which only requires a small amount of additional computational expense, is more accurate at predicting currents and identifying divergences than the traditional model.

      This new model could help oceanographers make more accurate estimates from buoy data, which would enable them to more effectively monitor the transportation of biomass (such as Sargassum seaweed), carbon, plastics, oil, and nutrients in the ocean. This information is also important for understanding and tracking climate change.

      “Our method captures the physical assumptions more appropriately and more accurately. In this case, we know a lot of the physics already. We are giving the model a little bit of that information so it can focus on learning the things that are important to us, like what are the currents away from the buoys, or what is this divergence and where is it happening?” says senior author Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.

      Broderick’s co-authors include lead author Renato Berlinghieri, an electrical engineering and computer science graduate student; Brian L. Trippe, a postdoc at Columbia University; David R. Burt and Ryan Giordano, MIT postdocs; Kaushik Srinivasan, an assistant researcher in atmospheric and ocean sciences at the University of California at Los Angeles; Tamay Özgökmen, professor in the Department of Ocean Sciences at the University of Miami; and Junfei Xia, a graduate student at the University of Miami. The research will be presented at the International Conference on Machine Learning.

      Diving into the data

      Oceanographers use data on buoy velocity to predict ocean currents and identify “divergences” where water rises to the surface or sinks deeper.

      To estimate currents and find divergences, oceanographers have used a machine-learning technique known as a Gaussian process, which can make predictions even when data are sparse. To work well in this case, the Gaussian process must make assumptions about the data to generate a prediction.

      A standard way of applying a Gaussian process to oceans data assumes the latitude and longitude components of the current are unrelated. But this assumption isn’t physically accurate. For instance, this existing model implies that a current’s divergence and its vorticity (a whirling motion of fluid) operate on the same magnitude and length scales. Ocean scientists know this is not true, Broderick says. The previous model also assumes the frame of reference matters, which means fluid would behave differently in the latitude versus the longitude direction.

      “We were thinking we could address these problems with a model that incorporates the physics,” she says.

      They built a new model that uses what is known as a Helmholtz decomposition to accurately represent the principles of fluid dynamics. This method models an ocean current by breaking it down into a vorticity component (which captures the whirling motion) and a divergence component (which captures water rising or sinking).

      In this way, they give the model some basic physics knowledge that it uses to make more accurate predictions.

      This new model utilizes the same data as the old model. And while their method can be more computationally intensive, the researchers show that the additional cost is relatively small.

      Buoyant performance

      They evaluated the new model using synthetic and real ocean buoy data. Because the synthetic data were fabricated by the researchers, they could compare the model’s predictions to ground-truth currents and divergences. But simulation involves assumptions that may not reflect real life, so the researchers also tested their model using data captured by real buoys released in the Gulf of Mexico.

      This shows the trajectories of approximately 300 buoys released during the Grand LAgrangian Deployment (GLAD) in the Gulf of Mexico in the summer of 2013, to learn about ocean surface currents around the Deepwater Horizon oil spill site. The small, regular clockwise rotations are due to Earth’s rotation.Credit: Consortium of Advanced Research for Transport of Hydrocarbons in the Environment

      In each case, their method demonstrated superior performance for both tasks, predicting currents and identifying divergences, when compared to the standard Gaussian process and another machine-learning approach that used a neural network. For example, in one simulation that included a vortex adjacent to an ocean current, the new method correctly predicted no divergence while the previous Gaussian process method and the neural network method both predicted a divergence with very high confidence.

      The technique is also good at identifying vortices from a small set of buoys, Broderick adds.

      Now that they have demonstrated the effectiveness of using a Helmholtz decomposition, the researchers want to incorporate a time element into their model, since currents can vary over time as well as space. In addition, they want to better capture how noise impacts the data, such as winds that sometimes affect buoy velocity. Separating that noise from the data could make their approach more accurate.

      “Our hope is to take this noisily observed field of velocities from the buoys, and then say what is the actual divergence and actual vorticity, and predict away from those buoys, and we think that our new technique will be helpful for this,” she says.

      “The authors cleverly integrate known behaviors from fluid dynamics to model ocean currents in a flexible model,” says Massimiliano Russo, an associate biostatistician at Brigham and Women’s Hospital and instructor at Harvard Medical School, who was not involved with this work. “The resulting approach retains the flexibility to model the nonlinearity in the currents but can also characterize phenomena such as vortices and connected currents that would only be noticed if the fluid dynamic structure is integrated into the model. This is an excellent example of where a flexible model can be substantially improved with a well thought and scientifically sound specification.”

      This research is supported, in part, by the Office of Naval Research, a National Science Foundation (NSF) CAREER Award, and the Rosenstiel School of Marine, Atmospheric, and Earth Science at the University of Miami. More