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      in Data Management & Statistics

      The tenured engineers of 2023

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      In 2023, MIT granted tenure to nine faculty members across the School of Engineering. This year’s tenured engineers hold appointments in the departments of Biological Engineering, Civil and Environmental Engineering, Electrical Engineering and Computer Science (which reports jointly to the School of Engineering and MIT Schwarzman College of Computing), Materials Science and Engineering, and Mechanical Engineering, as well as the Institute for Medical Engineering and Science (IMES).

      “I am truly inspired by this remarkable group of talented faculty members,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “The work they are doing, both in the lab and in the classroom, has made a tremendous impact at MIT and in the wider world. Their important research has applications in a diverse range of fields and industries. I am thrilled to congratulate them on the milestone of receiving tenure.”

      This year’s newly tenured engineering faculty include:

      Michael Birnbaum, Class of 1956 Career Development Professor, associate professor of biological engineering, and faculty member at the Koch Institute for Integrative Cancer Research at MIT, works on understanding and manipulating immune recognition in cancer and infections. By using a variety of techniques to study the antigen recognition of T cells, he and his team aim to develop the next generation of immunotherapies.  
      Tamara Broderick, associate professor of electrical engineering and computer science and member of the MIT Laboratory for Information and Decision Systems (LIDS) and the MIT Institute for Data, Systems, and Society (IDSS), works to provide fast and reliable quantification of uncertainty and robustness in modern data analysis procedures. Broderick and her research group develop data analysis tools with applications in fields, including genetics, economics, and assistive technology. 
      Tal Cohen, associate professor of civil and environmental engineering and mechanical engineering, uses nonlinear solid mechanics to understand how materials behave under extreme conditions. By studying material instabilities, extreme dynamic loading conditions, growth, and chemical coupling, Cohen and her team combine theoretical models and experiments to shape our understanding of the observed phenomena and apply those insights in the design and characterization of material systems. 
      Betar Gallant, Class of 1922 Career Development Professor and associate professor of mechanical engineering, develops advanced materials and chemistries for next-generation lithium-ion and lithium primary batteries and electrochemical carbon dioxide mitigation technologies. Her group’s work could lead to higher-energy and more sustainable batteries for electric vehicles, longer-lasting implantable medical devices, and new methods of carbon capture and conversion. 
      Rafael Jaramillo, Thomas Lord Career Development Professor and associate professor of materials science and engineering, studies the synthesis, properties, and applications of electronic materials, particularly chalcogenide compound semiconductors. His work has applications in microelectronics, integrated photonics, telecommunications, and photovoltaics. 
      Benedetto Marelli, associate professor of civil and environmental engineering, conducts research on the synthesis, assembly, and nanomanufacturing of structural biopolymers. He and his research team develop biomaterials for applications in agriculture, food security, and food safety. 
      Ellen Roche, Latham Family Career Development Professor, an associate professor of mechanical engineering, and a core faculty of IMES, designs and develops implantable, biomimetic therapeutic devices and soft robotics that mechanically assist and repair tissue, deliver therapies, and enable enhanced preclinical testing. Her devices have a wide range of applications in human health, including cardiovascular and respiratory disease. 
      Serguei Saavedra, associate professor of civil and environmental engineering, uses systems thinking, synthesis, and mathematical modeling to study the persistence of ecological systems under changing environments. His theoretical research is used to develop hypotheses and corroborate predictions of how ecological systems respond to climate change. 
      Justin Solomon, associate professor of electrical engineering and computer science and member of the MIT Computer Science and Artificial Intelligence Laboratory and MIT Center for Computational Science and Engineering, works at the intersection of geometry, large-scale optimization, computer graphics, and machine learning. His research has diverse applications in machine learning, computer graphics, and geometric data processing.  More

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      in Data Management & Statistics

      The curse of variety in transportation systems

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      Cathy Wu has always delighted in systems that run smoothly. In high school, she designed a project to optimize the best route for getting to class on time. Her research interests and career track are evidence of a propensity for organizing and optimizing, coupled with a strong sense of responsibility to contribute to society instilled by her parents at a young age.

      As an undergraduate at MIT, Wu explored domains like agriculture, energy, and education, eventually homing in on transportation. “Transportation touches each of our lives,” she says. “Every day, we experience the inefficiencies and safety issues as well as the environmental harms associated with our transportation systems. I believe we can and should do better.”

      But doing so is complicated. Consider the long-standing issue of traffic systems control. Wu explains that it is not one problem, but more accurately a family of control problems impacted by variables like time of day, weather, and vehicle type — not to mention the types of sensing and communication technologies used to measure roadway information. Every differentiating factor introduces an exponentially larger set of control problems. There are thousands of control-problem variations and hundreds, if not thousands, of studies and papers dedicated to each problem. Wu refers to the sheer number of variations as the curse of variety — and it is hindering innovation.

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      “To prove that a new control strategy can be safely deployed on our streets can take years. As time lags, we lose opportunities to improve safety and equity while mitigating environmental impacts. Accelerating this process has huge potential,” says Wu.  

      Which is why she and her group in the MIT Laboratory for Information and Decision Systems are devising machine learning-based methods to solve not just a single control problem or a single optimization problem, but families of control and optimization problems at scale. “In our case, we’re examining emerging transportation problems that people have spent decades trying to solve with classical approaches. It seems to me that we need a different approach.”

      Optimizing intersections

      Currently, Wu’s largest research endeavor is called Project Greenwave. There are many sectors that directly contribute to climate change, but transportation is responsible for the largest share of greenhouse gas emissions — 29 percent, of which 81 percent is due to land transportation. And while much of the conversation around mitigating environmental impacts related to mobility is focused on electric vehicles (EVs), electrification has its drawbacks. EV fleet turnover is time-consuming (“on the order of decades,” says Wu), and limited global access to the technology presents a significant barrier to widespread adoption.

      Wu’s research, on the other hand, addresses traffic control problems by leveraging deep reinforcement learning. Specifically, she is looking at traffic intersections — and for good reason. In the United States alone, there are more than 300,000 signalized intersections where vehicles must stop or slow down before re-accelerating. And every re-acceleration burns fossil fuels and contributes to greenhouse gas emissions.

      Highlighting the magnitude of the issue, Wu says, “We have done preliminary analysis indicating that up to 15 percent of land transportation CO2 is wasted through energy spent idling and re-accelerating at intersections.”

      To date, she and her group have modeled 30,000 different intersections across 10 major metropolitan areas in the United States. That is 30,000 different configurations, roadway topologies (e.g., grade of road or elevation), different weather conditions, and variations in travel demand and fuel mix. Each intersection and its corresponding scenarios represents a unique multi-agent control problem.

      Wu and her team are devising techniques that can solve not just one, but a whole family of problems comprised of tens of thousands of scenarios. Put simply, the idea is to coordinate the timing of vehicles so they arrive at intersections when traffic lights are green, thereby eliminating the start, stop, re-accelerate conundrum. Along the way, they are building an ecosystem of tools, datasets, and methods to enable roadway interventions and impact assessments of strategies to significantly reduce carbon-intense urban driving.

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      Their collaborator on the project is the Utah Department of Transportation, which Wu says has played an essential role, in part by sharing data and practical knowledge that she and her group otherwise would not have been able to access publicly.

      “I appreciate industry and public sector collaborations,” says Wu. “When it comes to important societal problems, one really needs grounding with practitioners. One needs to be able to hear the perspectives in the field. My interactions with practitioners expand my horizons and help ground my research. You never know when you’ll hear the perspective that is the key to the solution, or perhaps the key to understanding the problem.”

      Finding the best routes

      In a similar vein, she and her research group are tackling large coordination problems. For example, vehicle routing. “Every day, delivery trucks route more than a hundred thousand packages for the city of Boston alone,” says Wu. Accomplishing the task requires, among other things, figuring out which trucks to use, which packages to deliver, and the order in which to deliver them as efficiently as possible. If and when the trucks are electrified, they will need to be charged, adding another wrinkle to the process and further complicating route optimization.

      The vehicle routing problem, and therefore the scope of Wu’s work, extends beyond truck routing for package delivery. Ride-hailing cars may need to pick up objects as well as drop them off; and what if delivery is done by bicycle or drone? In partnership with Amazon, for example, Wu and her team addressed routing and path planning for hundreds of robots (up to 800) in their warehouses.

      Every variation requires custom heuristics that are expensive and time-consuming to develop. Again, this is really a family of problems — each one complicated, time-consuming, and currently unsolved by classical techniques — and they are all variations of a central routing problem. The curse of variety meets operations and logistics.

      By combining classical approaches with modern deep-learning methods, Wu is looking for a way to automatically identify heuristics that can effectively solve all of these vehicle routing problems. So far, her approach has proved successful.

      “We’ve contributed hybrid learning approaches that take existing solution methods for small problems and incorporate them into our learning framework to scale and accelerate that existing solver for large problems. And we’re able to do this in a way that can automatically identify heuristics for specialized variations of the vehicle routing problem.” The next step, says Wu, is applying a similar approach to multi-agent robotics problems in automated warehouses.

      Wu and her group are making big strides, in part due to their dedication to use-inspired basic research. Rather than applying known methods or science to a problem, they develop new methods, new science, to address problems. The methods she and her team employ are necessitated by societal problems with practical implications. The inspiration for the approach? None other than Louis Pasteur, who described his research style in a now-famous article titled “Pasteur’s Quadrant.” Anthrax was decimating the sheep population, and Pasteur wanted to better understand why and what could be done about it. The tools of the time could not solve the problem, so he invented a new field, microbiology, not out of curiosity but out of necessity. More

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      in Data Management & Statistics

      A simpler method for learning to control a robot

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      Researchers from MIT and Stanford University have devised a new machine-learning approach that could be used to control a robot, such as a drone or autonomous vehicle, more effectively and efficiently in dynamic environments where conditions can change rapidly.

      This technique could help an autonomous vehicle learn to compensate for slippery road conditions to avoid going into a skid, allow a robotic free-flyer to tow different objects in space, or enable a drone to closely follow a downhill skier despite being buffeted by strong winds.

      The researchers’ approach incorporates certain structure from control theory into the process for learning a model in such a way that leads to an effective method of controlling complex dynamics, such as those caused by impacts of wind on the trajectory of a flying vehicle. One way to think about this structure is as a hint that can help guide how to control a system.

      “The focus of our work is to learn intrinsic structure in the dynamics of the system that can be leveraged to design more effective, stabilizing controllers,” says Navid Azizan, the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), and a member of the Laboratory for Information and Decision Systems (LIDS). “By jointly learning the system’s dynamics and these unique control-oriented structures from data, we’re able to naturally create controllers that function much more effectively in the real world.”

      Using this structure in a learned model, the researchers’ technique immediately extracts an effective controller from the model, as opposed to other machine-learning methods that require a controller to be derived or learned separately with additional steps. With this structure, their approach is also able to learn an effective controller using fewer data than other approaches. This could help their learning-based control system achieve better performance faster in rapidly changing environments.

      “This work tries to strike a balance between identifying structure in your system and just learning a model from data,” says lead author Spencer M. Richards, a graduate student at Stanford University. “Our approach is inspired by how roboticists use physics to derive simpler models for robots. Physical analysis of these models often yields a useful structure for the purposes of control — one that you might miss if you just tried to naively fit a model to data. Instead, we try to identify similarly useful structure from data that indicates how to implement your control logic.”

      Additional authors of the paper are Jean-Jacques Slotine, professor of mechanical engineering and of brain and cognitive sciences at MIT, and Marco Pavone, associate professor of aeronautics and astronautics at Stanford. The research will be presented at the International Conference on Machine Learning (ICML).

      Learning a controller

      Determining the best way to control a robot to accomplish a given task can be a difficult problem, even when researchers know how to model everything about the system.

      A controller is the logic that enables a drone to follow a desired trajectory, for example. This controller would tell the drone how to adjust its rotor forces to compensate for the effect of winds that can knock it off a stable path to reach its goal.

      This drone is a dynamical system — a physical system that evolves over time. In this case, its position and velocity change as it flies through the environment. If such a system is simple enough, engineers can derive a controller by hand. 

      Modeling a system by hand intrinsically captures a certain structure based on the physics of the system. For instance, if a robot were modeled manually using differential equations, these would capture the relationship between velocity, acceleration, and force. Acceleration is the rate of change in velocity over time, which is determined by the mass of and forces applied to the robot.

      But often the system is too complex to be exactly modeled by hand. Aerodynamic effects, like the way swirling wind pushes a flying vehicle, are notoriously difficult to derive manually, Richards explains. Researchers would instead take measurements of the drone’s position, velocity, and rotor speeds over time, and use machine learning to fit a model of this dynamical system to the data. But these approaches typically don’t learn a control-based structure. This structure is useful in determining how to best set the rotor speeds to direct the motion of the drone over time.

      Once they have modeled the dynamical system, many existing approaches also use data to learn a separate controller for the system.

      “Other approaches that try to learn dynamics and a controller from data as separate entities are a bit detached philosophically from the way we normally do it for simpler systems. Our approach is more reminiscent of deriving models by hand from physics and linking that to control,” Richards says.

      Identifying structure

      The team from MIT and Stanford developed a technique that uses machine learning to learn the dynamics model, but in such a way that the model has some prescribed structure that is useful for controlling the system.

      With this structure, they can extract a controller directly from the dynamics model, rather than using data to learn an entirely separate model for the controller.

      “We found that beyond learning the dynamics, it’s also essential to learn the control-oriented structure that supports effective controller design. Our approach of learning state-dependent coefficient factorizations of the dynamics has outperformed the baselines in terms of data efficiency and tracking capability, proving to be successful in efficiently and effectively controlling the system’s trajectory,” Azizan says. 

      When they tested this approach, their controller closely followed desired trajectories, outpacing all the baseline methods. The controller extracted from their learned model nearly matched the performance of a ground-truth controller, which is built using the exact dynamics of the system.

      “By making simpler assumptions, we got something that actually worked better than other complicated baseline approaches,” Richards adds.

      The researchers also found that their method was data-efficient, which means it achieved high performance even with few data. For instance, it could effectively model a highly dynamic rotor-driven vehicle using only 100 data points. Methods that used multiple learned components saw their performance drop much faster with smaller datasets.

      This efficiency could make their technique especially useful in situations where a drone or robot needs to learn quickly in rapidly changing conditions.

      Plus, their approach is general and could be applied to many types of dynamical systems, from robotic arms to free-flying spacecraft operating in low-gravity environments.

      In the future, the researchers are interested in developing models that are more physically interpretable, and that would be able to identify very specific information about a dynamical system, Richards says. This could lead to better-performing controllers.

      “Despite its ubiquity and importance, nonlinear feedback control remains an art, making it especially suitable for data-driven and learning-based methods. This paper makes a significant contribution to this area by proposing a method that jointly learns system dynamics, a controller, and control-oriented structure,” says Nikolai Matni, an assistant professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania, who was not involved with this work. “What I found particularly exciting and compelling was the integration of these components into a joint learning algorithm, such that control-oriented structure acts as an inductive bias in the learning process. The result is a data-efficient learning process that outputs dynamic models that enjoy intrinsic structure that enables effective, stable, and robust control. While the technical contributions of the paper are excellent themselves, it is this conceptual contribution that I view as most exciting and significant.”

      This research is supported, in part, by the NASA University Leadership Initiative and the Natural Sciences and Engineering Research Council of Canada. 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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      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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      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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      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