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      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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      Building a playbook for elite-level sports

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      “All I did was swim,” says Jerry Lu, recalling his teenage years as a competitive swimmer. “From age 12 to 19, it was close to 30 hours a week of training.” Although Lu no longer competes himself, his understanding of the dedication and impeccable technique required in elite sports continues to shape his path as a master’s student at the MIT Sloan School of Management.

      As an undergraduate at the University of Virginia, Lu majored in systems and information engineering and economics. He had stopped swimming competitively, but he stayed connected to the sport as a technical performance consultant for the university’s nationally ranked swim team. Under his advisor, Ken Ono, Lu built a methodology of analyzing data from sensors worn by swimmers to improve their individual performance. By looking at an athlete’s propulsion and drag data over the course of a race, Lu can advise them on where they can shave off tenths of a second simply by adjusting their stroke to be more efficient.

      That experience inspired Lu to pursue a career in other aspects of sports. At MIT he’s pursuing a master’s in finance to build the analytical skills necessary to enable the sustainability of sports that don’t already enjoy the major commercial success of, say, football or basketball. It’s especially a challenge for Olympic sports, such as swimming, which struggle for commercial ventures outside of Olympic years.

      “My work in swimming is focused on athlete performance to win, but the definition of winning is different for a sport as a whole, and for an organization,” Lu says. “Not only do you need to win medals, a big part of it is how you allocate money because you also need to grow your sport.”

      At MIT, Lu is building a playbook for high-performance sports from both an athletic and financial perspective. He’s been gaining exposure to additional elite sports by working with MIT’s Sports Lab under Professor Anette “Peko” Hosoi. His work there isn’t a requirement for his master’s program, but Lu appreciates that the program’s flexibility allows him time to pursue research that interests him, alongside the required curriculum.

      “I’m quite lucky to be here in the sense that MIT is known to train great people in engineering,  science, or business, but also people with unique passions,” says Lu. “People that love football drafting, people that love to understand how you throw a curveball — they use their knowledge in very unexpected ways, and that’s when innovation happens.”

      Lu’s research with the Sports Lab focuses on optimizing strategies for aesthetic sports, such as figure skating or snowboarding, which are judged very differently than swimming is. Instead of figuring out how to move faster, athletes are interested in structuring routines that net them the most points from a panel of judges. Modelling techniques can be helpful for figuring out how to put together routines to maximize an athlete’s abilities, and also to predict how a judge might assign points based on how or when a skill is demonstrated. Optimizing both athletic performance and judge psychology is a challenge, it’s this type of innovation that excites him. He hopes more sporting organizations will adopt similar data-driven strategies in the future.

      When asked where he’d like to end up after finishing his degree, “The sport industry is the natural choice,” Lu says. Though he is certain his career will lead to sports eventually, he is still open to exploring new paths. This summer he will be a trading intern at Citadel Securities to apply the concepts learned in his degree program courses. He’s also picked up sailing since coming to MIT, already reaching the highest amateur rating in under a year. Lu consistently strives for excellence, whether in himself or for those he works with.

      Since graduating from UVA, Lu has continued to work with swimmers, including national champions and Olympic medalists, as a technical performance consultant. He’s also branched out into another Olympic sport, triathlon. Lu describes it as a side gig, but he’s deeply invested in the athletes he works with, even taking trips to the Olympic Training Center to collect data and help them build strategies for improvement.

      “The most fun part is actually interacting with the athletes and engaging and understanding how they think,” says Lu. “It’s easier for me to do so than others, because if you’ve never swam before and you’ve never trained as an elite athlete before, it’s hard to understand what exactly you can and cannot do and how to communicate these things to a coach or an athlete.” More

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      Using data to write songs for progress

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      A three-year recipient of MIT’s Emerson Classical Vocal Scholarships, senior Ananya Gurumurthy recalls getting ready to step onto the Carnegie Hall stage to sing a Mozart opera that she once sang with the New York All-State Choir. The choir conductor reminded her to articulate her words and to engage her diaphragm.

      “If you don’t project your voice, how are people going to hear you when you perform?” Gurumurthy recalls her conductor telling her. “This is your moment, your chance to connect with such a tremendous audience.”

      Gurumurthy reflects on the universal truth of those words as she adds her musical talents to her math and computer science studies to campaign for social and economic justice.

      The daughter of immigrants

      Growing up in Edgemont, New York, she was inspired to fight on behalf of others by her South Asian immigrant parents, who came to the United States in the 1980s. Her father is a management consultant and her mother has experience as an investment banker.

      “They came barely 15 years after the passage of the 1965 Immigration and Nationality Act, which removed national origin quotas from the American immigration system,” she says. “I would not be here if it had not been for the Civil Rights Movement, which preceded both me and my parents.”

      Her parents told her about their new home’s anti-immigrant sentiments; for example, her father was a graduate student in Dallas exiting a store when he was pelted with glass bottles and racial slurs.

      “I often consider the amount of bravery that it must have taken them to abandon everything they knew to immigrate to a new, but still imperfect, country in search of something better,” she says. “As a result, I have always felt so grounded in my identity both as a South Asian American and a woman of color. These identities have allowed me to think critically about how I can most effectively reform the institutions surrounding me.”

      Gurumurthy has been singing since she was 11, but in high school, she decided to also build her political voice by working for New York Senator Andrea Stewart-Cousins. At one point, Gurumurthy noted a log was kept for the subjects of constituent calls, such as “affordable housing” and  “infrastructure,” and it was then that she became aware that Stewart-Cousins would address the most pressing of these callers’ issues before the Senate.

      “This experience was my first time witnessing how powerful the mobilization of constituents in vast numbers was for influencing meaningful legislative change,” says Gurumurthy.

      After she began applying her math skills to political campaigns, Gurumurthy was soon tapped to run analytics for the Democratic National Committee’s (DNC) midterm election initiative. As a lead analyst for the New York DNC, she adapted an interactive activation-competition (IAC) model to understand voting patterns in the 2018 and 2020 elections. She collected data from public voting records to predict how constituents would cast their ballots and used an IAC algorithm to strategize alongside grassroots organizations and allocate resources to empower historically disenfranchised groups in municipal, state, and federal elections to encourage them to vote.

      Research and student organizing at MIT

      When she arrived at MIT in 2019 to study mathematics with computer science, along with minors in music and economics, she admits she was saddled with the naïve notion that she would “build digital tools that could single-handedly alleviate all of the collective pressures of systemic injustice in this country.” 

      Since then, she has learned to create what she calls “a more nuanced view.” She picked up data analytics skills to build mobilization platforms for organizations that pursued social and economic justice, including working in Fulton County, Georgia, with Fair Fight Action (through the Kelly-Douglas Fund Scholarship) to analyze patterns of voter suppression, and MIT’s ethics laboratories in the Computer Science and Artificial Intelligence Laboratory to build symbolic artificial intelligence protocols to better understand bias in artificial intelligence algorithms. For her work on the International Monetary Fund (through the MIT Washington Summer Internship Program), Gurumurthy was awarded second place for the 2022 S. Klein Prize in Technical Writing for her paper “The Rapid Rise of Cryptocurrency.”

      “The outcomes of each project gave me more hope to begin the next because I could see the impact of these digital tools,” she says. “I saw people feel empowered to use their voices whether it was voting for the first time, protesting exploitative global monetary policy, or fighting gender discrimination. I’ve been really fortunate to see the power of mathematical analysis firsthand.”

      “I have come to realize that the constructive use of technology could be a powerful voice of resistance against injustice,” she says. “Because numbers matter, and when people bear witness to them, they are pushed to take action in meaningful ways.”

      Hoping to make a difference in her own community, she joined several Institute committees. As co-chair of the Undergraduate Association’s education committee, she propelled MIT’s first-ever digital petition for grade transparency and worked with faculty members on Institute committees to ensure that all students were being provided adequate resources to participate in online education in the wake of the Covid-19 pandemic. The digital petition inspired her to begin a project, called Insite, to develop a more centralized digital means of data collection on student life at MIT to better inform policies made by its governing bodies. As Ring Committee chair, she ensured that the special traditions of the “Brass Rat” were made economically accessible to all class members by helping the committee nearly triple its financial aid budget. For her efforts at MIT, last May she received the William L. Stewart, Jr. Award for “[her] contributions [as] an individual student at MIT to extracurricular activities and student life.”

      Ananya plans on going to law school after graduation, to study constitutional law so that she can use her technical background to build quantitative evidence in cases pertaining to voting rights, social welfare, and ethical technology, and set legal standards ”for the humane use of data,” she says.

      “In building digital tools for a variety of social and economic justice organizations, I hope that we can challenge our existing systems of power and realize the progress we so dearly need to witness. There is strength in numbers, both algorithmically and organizationally. I believe it is our responsibility to simultaneously use these strengths to change the world.”

      Her ambitions, however, began when she began singing lessons when she was 11; without her background as a vocalist, she says she would be voiceless.

      “Operatic performance has given me the ability to truly step into my character and convey powerful emotions in my performance. In the process, I have realized that my voice is most powerful when it reflects my true convictions, whether I am performing or publicly speaking. I truly believe that this honesty has allowed me to become an effective community organizer. I’d like to believe that this voice is what compels those around me to act.”

      Private musical study is available for students through the Emerson/Harris Program, which offers merit-based financial awards to students of outstanding achievement on their instruments or voice in classical, jazz, or world music. The Emerson/Harris Program is funded by the late Cherry L. Emerson Jr. SM ’41, in response to an appeal from Associate Provost Ellen T. Harris (Class of 1949 professor emeritus of music). More

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      Joining the battle against health care bias

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      Medical researchers are awash in a tsunami of clinical data. But we need major changes in how we gather, share, and apply this data to bring its benefits to all, says Leo Anthony Celi, principal research scientist at the MIT Laboratory for Computational Physiology (LCP). 

      One key change is to make clinical data of all kinds openly available, with the proper privacy safeguards, says Celi, a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC) in Boston. Another key is to fully exploit these open data with multidisciplinary collaborations among clinicians, academic investigators, and industry. A third key is to focus on the varying needs of populations across every country, and to empower the experts there to drive advances in treatment, says Celi, who is also an associate professor at Harvard Medical School. 

      In all of this work, researchers must actively seek to overcome the perennial problem of bias in understanding and applying medical knowledge. This deeply damaging problem is only heightened with the massive onslaught of machine learning and other artificial intelligence technologies. “Computers will pick up all our unconscious, implicit biases when we make decisions,” Celi warns.

      Play video

      Sharing medical data 

      Founded by the LCP, the MIT Critical Data consortium builds communities across disciplines to leverage the data that are routinely collected in the process of ICU care to understand health and disease better. “We connect people and align incentives,” Celi says. “In order to advance, hospitals need to work with universities, who need to work with industry partners, who need access to clinicians and data.” 

      The consortium’s flagship project is the MIMIC (medical information marked for intensive care) ICU database built at BIDMC. With about 35,000 users around the world, the MIMIC cohort is the most widely analyzed in critical care medicine. 

      International collaborations such as MIMIC highlight one of the biggest obstacles in health care: most clinical research is performed in rich countries, typically with most clinical trial participants being white males. “The findings of these trials are translated into treatment recommendations for every patient around the world,” says Celi. “We think that this is a major contributor to the sub-optimal outcomes that we see in the treatment of all sorts of diseases in Africa, in Asia, in Latin America.” 

      To fix this problem, “groups who are disproportionately burdened by disease should be setting the research agenda,” Celi says. 

      That’s the rule in the “datathons” (health hackathons) that MIT Critical Data has organized in more than two dozen countries, which apply the latest data science techniques to real-world health data. At the datathons, MIT students and faculty both learn from local experts and share their own skill sets. Many of these several-day events are sponsored by the MIT Industrial Liaison Program, the MIT International Science and Technology Initiatives program, or the MIT Sloan Latin America Office. 

      Datathons are typically held in that country’s national language or dialect, rather than English, with representation from academia, industry, government, and other stakeholders. Doctors, nurses, pharmacists, and social workers join up with computer science, engineering, and humanities students to brainstorm and analyze potential solutions. “They need each other’s expertise to fully leverage and discover and validate the knowledge that is encrypted in the data, and that will be translated into the way they deliver care,” says Celi. 

      “Everywhere we go, there is incredible talent that is completely capable of designing solutions to their health-care problems,” he emphasizes. The datathons aim to further empower the professionals and students in the host countries to drive medical research, innovation, and entrepreneurship.

      Play video

      Fighting built-in bias 

      Applying machine learning and other advanced data science techniques to medical data reveals that “bias exists in the data in unimaginable ways” in every type of health product, Celi says. Often this bias is rooted in the clinical trials required to approve medical devices and therapies. 

      One dramatic example comes from pulse oximeters, which provide readouts on oxygen levels in a patient’s blood. It turns out that these devices overestimate oxygen levels for people of color. “We have been under-treating individuals of color because the nurses and the doctors have been falsely assured that their patients have adequate oxygenation,” he says. “We think that we have harmed, if not killed, a lot of individuals in the past, especially during Covid, as a result of a technology that was not designed with inclusive test subjects.” 

      Such dangers only increase as the universe of medical data expands. “The data that we have available now for research is maybe two or three levels of magnitude more than what we had even 10 years ago,” Celi says. MIMIC, for example, now includes terabytes of X-ray, echocardiogram, and electrocardiogram data, all linked with related health records. Such enormous sets of data allow investigators to detect health patterns that were previously invisible. 

      “But there is a caveat,” Celi says. “It is trivial for computers to learn sensitive attributes that are not very obvious to human experts.” In a study released last year, for instance, he and his colleagues showed that algorithms can tell if a chest X-ray image belongs to a white patient or person of color, even without looking at any other clinical data. 

      “More concerningly, groups including ours have demonstrated that computers can learn easily if you’re rich or poor, just from your imaging alone,” Celi says. “We were able to train a computer to predict if you are on Medicaid, or if you have private insurance, if you feed them with chest X-rays without any abnormality. So again, computers are catching features that are not visible to the human eye.” And these features may lead algorithms to advise against therapies for people who are Black or poor, he says. 

      Opening up industry opportunities 

      Every stakeholder stands to benefit when pharmaceutical firms and other health-care corporations better understand societal needs and can target their treatments appropriately, Celi says. 

      “We need to bring to the table the vendors of electronic health records and the medical device manufacturers, as well as the pharmaceutical companies,” he explains. “They need to be more aware of the disparities in the way that they perform their research. They need to have more investigators representing underrepresented groups of people, to provide that lens to come up with better designs of health products.” 

      Corporations could benefit by sharing results from their clinical trials, and could immediately see these potential benefits by participating in datathons, Celi says. “They could really witness the magic that happens when that data is curated and analyzed by students and clinicians with different backgrounds from different countries. So we’re calling out our partners in the pharmaceutical industry to organize these events with us!”  More

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      Driving toward data justice

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      As a person with a mixed-race background who has lived in four different cities, Amelia Dogan describes her early life as “growing up in a lot of in-betweens.” Now an MIT senior, she continues to link different perspectives together, working at the intersection of urban planning, computer science, and social justice.

      Dogan was born in Canada but spent her high school years in Philadelphia, where she developed a strong affinity for the city.  

      “I love Philadelphia to death,” says Dogan. “It’s my favorite place in the world. The energy in the city is amazing — I’m so sad I wasn’t there for the Super Bowl this year — but it is a city with really big disparities. That drives me to do the research that I do and shapes the things that I care about.”

      Dogan is double-majoring in urban science and planning with computer science and in American studies. She decided on the former after participating in the pre-orientation program offered by the Department of Urban Studies and Planning, which provides an introduction to both the department and the city of Boston. She followed that up with a UROP research project with the West Philadelphia Landscape Project, putting together historical census data on housing and race to find patterns for use in community advocacy.

      After taking WGS.231 (Writing About Race), a course offered by the Program in Women’s and Gender Studies, her first year at MIT, Dogan realized there was a lot of crosstalk between urban planning, computer science, and the social sciences.

      “There’s a lot of critical social theory that I want to have background in to make me a better planner or a better computer scientist,” says Dogan. “There’s also a lot of issues around fairness and participation in computer science, and a lot of computer scientists are trying to reinvent the wheel when there’s already really good, critical social science research and theory behind this.”

      Data science and feminism

      Dogan’s first year at MIT was interrupted by the onset of the Covid-19 pandemic, but there was a silver lining. An influx of funding to keep students engaged while attending school virtually enabled her to join the Data + Feminism Lab to work on a case study examining three places in Philadelphia with historical names that were renamed after activist efforts.

      In her first year at MIT, Dogan worked several UROPs to hone her own skills and find the best research fit. Besides the West Philadelphia Land Project, she worked on two projects within the MIT Sloan School of Management. The first involved searching for connections between entrepreneurship and immigration among Fortune 500 founders. The second involved interviewing warehouse workers and writing a report on their quality of life.

      Dogan has now spent three years in the Data + Feminism Lab under Associate Professor Catherine D’Ignazio, where she is particularly interested in how technology can be used by marginalized communities to invert historical power imbalances. A key concept in the lab’s work is that of counterdata, which are produced by civil society groups or individuals in order to counter missing data or to challenge existing official data.

      Most recently, she completed a SuperUROP project investigating how femicide data activist organizations use social media. She analyzed 600 social media posts by organizations across the U.S. and Canada. The work built off the lab’s greater body of work with these groups, which Dogan has contributed to by annotating news articles for machine-learning models.

      “Catherine works a lot at the intersection of data issues and feminism. It just seemed like the right fit for me,” says Dogan. “She’s my academic advisor, she’s my research advisor, and is also a really good mentor.”

      Advocating for the student experience

      Outside of the classroom, Dogan is a strong advocate for improving the student experience, particularly when it intersects with identity. An executive board member of the Asian American Initiative (AAI), she also sits on the student advisory council for the Office of Minority Education.

      “Doing that institutional advocacy has been important to me, because it’s for things that I expected coming into college and had not come in prepared to fight for,” says Dogan. As a high schooler, she participated in programs run by the University of Pennsylvania’s Pan-Asian American Community House and was surprised to find that MIT did not have an equivalent organization.

      “Building community based upon identity is something that I’ve been really passionate about,” says Dogan. “For the past two years, I’ve been working with AAI on a list of recommendations for MIT. I’ve talked to alums from the ’90s who were a part of an Asian American caucus who were asking for the same things.”

      She also holds a leadership role with MIXED @ MIT, a student group focused on creating space for mixed-heritage students to explore and discuss their identities.

      Following graduation, Dogan plans to pursue a PhD in information science at the University of Washington. Her breadth of skills has given her a range of programs to choose from. No matter where she goes next, Dogan wants to pursue a career where she can continue to make a tangible impact.

      “I would love to be doing community-engaged research around data justice, using citizen science and counterdata for policy and social change,” she says. More

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      Illuminating the money trail

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      You may not know this, but the U.S. imposes a 12.5 percent import tariff on imported flashlights. However, for a product category the federal government describes as “portable electric lamps designed to function by their own source of energy, other than flashlights,” the import tariff is just 3.5 percent.

      At a glance, this seems inexplicable. Why is one kind of self-powered portable light taxed more heavily than another? According to MIT political science professor In Song Kim, a policy discrepancy like this often stems from the difference in firms’ political power, as well as the extent to which firms are empowered by global production networks. This is a subject Kim has spent years examining in detail, producing original scholarly results while opening up a wealth of big data about politics to the public.

      “We all understand companies as being important economic agents,” Kim says. “But companies are political agents, too. They are very important political actors.”

      In particular, Kim’s work has illuminated the effects of lobbying upon U.S. trade policy. International trade is often presented as an unalloyed good, opening up markets and fueling growth. Beyond that, trade issues are usually described at the industry level; we hear about what the agriculture lobby or auto industry wants. But in reality, different firms want different things, even within the same industry.

      As Kim’s work shows, most firms lobby for policies pertaining to specific components of their products, and trade policy consists heavily of carve-outs for companies, not industry-wide standards. Firms making non-flashlight portable lights, it would seem, are good at lobbying, but the benefits clearly do not carry over to all portable light makers, as long as products are not perfect substitutes for each other. Meanwhile, as Kim’s research also shows, lobbying helps firms grow faster in size, even as lobbying-influenced policies may slow down the economy as a whole.

      “All our existing theories suggest that trade policy is a public good, in the sense that the benefits of open trade, the gains from trade, will be enjoyed by the public and will benefit the country as a whole,” Kim says. “But what I’ve learned is that trade policies are very, very granular. It’s become obvious to me that trade is no longer a public good. It’s actually a private good for individual companies.”

      Kim’s work includes over a dozen published journal articles over the last several years, several other forthcoming research papers, and a book he is currently writing. At the same time, Kim has created a public database, LobbyView, which tracks money in U.S. politics extending back to 1999. LobbyView, as an important collection of political information, has research, educational, and public-interest applications, enabling others, in academia or outside it, to further delve into the topic.

      “I want to contribute to the scholarly community, and I also want to create a public [resource] for our MIT community [and beyond], so we can all study politics through it,” Kim says.

      Keeping the public good in sight

      Kim grew up in South Korea, in a setting where politics was central to daily life. Kim’s grandfather, Kim jae-soon, was the Speaker of the National Assembly in South Korea from 1988 through 1990 and an important figure in the country’s government.

      “I’ve always been fascinated by politics,” says Kim, who remembers prominent political figures dropping by the family home when he was young. One of the principal lessons Kim learned about politics from his grandfather, however, was not about proximity to power, but the importance of public service. The enduring lesson of his family’s engagement with politics, Kim says, is that “I truly believe in contributing to the public good.”

      Kim’s found his own way of contributing to the public good not as a politician but as a scholar of politics. Kim received his BA in political science from Yonsei University in Seoul but decided he wanted to pursue graduate studies in the U.S. He earned an MA in law and diplomacy from the Fletcher School of Tufts University, then an MA in political science at George Washington University. By this time, Kim had become focused on the quantitative analysis of trade policy; for his PhD work, he attended Princeton University and was awarded his doctorate in 2014, joining the MIT faculty that year.

      Among the key pieces of research Kim has published, one paper, “Political Cleavages within Industry: Firm-level Lobbying for Trade Liberalization,” published in the American Political Science Review and growing out of his dissertation research, helped show how remarkably specialized many trade policies are. As of 2017, the U.S. had almost 17,000 types of products it made tariff decisions about. Many of these are the component parts of a product; about two-thirds of international trade consists of manufactured components that get shipped around during the production process, rather than raw goods or finished products. That paper won the 2018 Michael Wallerstein Award for the best published article in political economy in the previous year.

      Another 2017 paper Kim co-authored, “The Charmed Life of Superstar Exporters,” from the Journal of Politics, provides more empirical evidence of the differences among firms within an industry. The “superstar” firms that are the largest exporters tend to lobby the most about trade politics; a firm’s characteristics reveal more about its preferences for open trade than the possibility that its industry as a whole will gain a comparative advantage internationally.

      Kim often uses large-scale data and computational methods to study international trade and trade politics. Still another paper he has co-authored, “Measuring Trade Profile with Granular Product-level Trade Data,” published in the American Journal of Political Science in 2020, traces trade relationships in highly specific terms. Looking at over 2 billion observations of international trade data, Kim developed an algorithm to group countries based on which products they import and export. The methodology helps researchers to learn about the highly different developmental paths that countries follow, and about the deepening international competition between countries such as the U.S. and China.

      At other times, Kim has analyzed who is influencing trade policy. His paper “Mapping Political Communities,” from the journal Political Analysis in 2021, looks at the U.S. Congress and uses mandatory reports filed by lobbyists to build a picture of which interests groups are most closely connected to which politicians.

      Kim has published all his papers while balancing both his scholarly research and the public launch of LobbyView, which occurred in 2018. He was awarded tenure by MIT in the spring of 2022. Currently he is an associate professor in the Department of Political Science and a faculty affiliate of the Institute for Data, Systems, and Society.

      By the book

      Kim has continued to explore firm-level lobbying dynamics, although his recent research runs in a few directions. In a 2021 working paper, Kim and co-author Federico Huneeus of the Central Bank of Chile built a model estimating that eliminating lobbying in the U.S. could increase productivity by as much as 6 percent.

      “Political rents [favorable policies] given to particular companies might introduce inefficiencies or a misallocation of resources in the economy,” Kim says. “You could allocate those resources to more productive although politically inactive firms, but now they’re given to less productive and yet politically active big companies, increasing market concentration and monopolies.”

      Kim is on sabbatical during the 2022-23 academic year, working on a book about the importance of firms’ political activities in trade policymaking. The book will have an expansive timeframe, dating back to ancient times, which underscores the salience of trade policy across eras. At the same time, the book will analyze the distinctive features of modern trade politics with deepening global production networks.

      “I’m trying to allow people to learn about the history of trade politics, to show how the politics have changed over time,” Kim says. “In doing that, I’m also highlighting the importance of firm-to-firm trade and the emergence of new trade coalitions among firms in different countries and industries that are linked through the global production chain.”

      While continuing his own scholarly research, Kim still leads LobbyView, which he views both as a big data resource for any scholars interested in money in politics and an excellent teaching resource for his MIT classes, as students can tap into it for projects and papers. LobbyView contains so much data, in fact, that part of the challenge is finding ways to mine it effectively.

      “It really offers me an opportunity to work with MIT students,” Kim says of LobbyView. “What I think I can contribute is to bring those technologies to our understanding of politics. Having this unique data set can really allow students here to use technology to learn about politics, and I believe that fits the MIT identity.” More

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      in Data Management & Statistics

      Helping the cause of environmental resilience

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      Haruko Wainwright, the Norman C. Rasmussen Career Development Professor in Nuclear Science and Engineering (NSE) and assistant professor in civil and environmental engineering at MIT, grew up in rural Japan, where many nuclear facilities are located. She remembers worrying about the facilities as a child. Wainwright was only 6 at the time of the Chernobyl accident in 1986, but still recollects it vividly.

      Those early memories have contributed to Wainwright’s determination to research how technologies can mold environmental resilience — the capability of mitigating the consequences of accidents and recovering from contamination.

      Wainwright believes that environmental monitoring can help improve resilience. She co-leads the U.S. Department of Energy (DOE)’s Advanced Long-term Environmental Monitoring Systems (ALTEMIS) project, which integrates technologies such as in situ sensors, geophysics, remote sensing, simulations, and artificial intelligence to establish new paradigms for monitoring. The project focuses on soil and groundwater contamination at more than 100 U.S. sites that were used for nuclear weapons production.

      As part of this research, which was featured last year in Environmental Science & Technology Journal, Wainwright is working on a machine learning framework for improving environmental monitoring strategies. She hopes the ALTEMIS project will enable the rapid detection of anomalies while ensuring the stability of residual contamination and waste disposal facilities.

      Childhood in rural Japan

      Even as a child, Wainwright was interested in physics, history, and a variety of other subjects.

      But growing up in a rural area was not ideal for someone interested in STEM. There were no engineers or scientists in the community and no science museums, either. “It was not so cool to be interested in science, and I never talked about my interest with anyone,” Wainwright recalls.

      Television and books were the only door to the world of science. “I did not study English until middle school and I had never been on a plane until college. I sometimes find it miraculous that I am now working in the U.S. and teaching at MIT,” she says.

      As she grew a little older, Wainwright heard a lot of discussions about nuclear facilities in the region and many stories about Hiroshima and Nagasaki.

      At the same time, giants like Marie Curie inspired her to pursue science. Nuclear physics was particularly fascinating. “At some point during high school, I started wondering ‘what are radiations, what is radioactivity, what is light,’” she recalls. Reading Richard Feynman’s books and trying to understand quantum mechanics made her want to study physics in college.

      Pursuing research in the United States

      Wainwright pursued an undergraduate degree in engineering physics at Kyoto University. After two research internships in the United States, Wainwright was impressed by the dynamic and fast-paced research environment in the country.

      And compared to Japan, there were “more women in science and engineering,” Wainwright says. She enrolled at the University of California at Berkeley in 2005, where she completed her doctorate in nuclear engineering with minors in statistics and civil and environmental engineering.

      Before moving to MIT NSE in 2022, Wainwright was a staff scientist in the Earth and Environmental Area at Lawrence Berkeley National Laboratory (LBNL). She worked on a variety of topics, including radioactive contamination, climate science, CO2 sequestration, precision agriculture, and watershed science. Her time at LBNL helped Wainwright build a solid foundation about a variety of environmental sensors and monitoring and simulation methods across different earth science disciplines.   

      Empowering communities through monitoring

      One of the most compelling takeaways from Wainwright’s early research: People trust actual measurements and data as facts, even though they are skeptical about models and predictions. “I talked with many people living in Fukushima prefecture. Many of them have dosimeters and measure radiation levels on their own. They might not trust the government, but they trust their own data and are then convinced that it is safe to live there and to eat local food,” Wainwright says.

      She has been impressed that area citizens have gained significant knowledge about radiation and radioactivity through these efforts. “But they are often frustrated that people living far away, in cities like Tokyo, still avoid agricultural products from Fukushima,” Wainwright says.

      Wainwright thinks that data derived from environmental monitoring — through proper visualization and communication — can address misconceptions and fake news that often hurt people near contaminated sites.

      Wainwright is now interested in how these technologies — tested with real data at contaminated sites — can be proactively used for existing and future nuclear facilities “before contamination happens,” as she explored for Nuclear News. “I don’t think it is a good idea to simply dismiss someone’s concern as irrational. Showing credible data has been much more effective to provide assurance. Or a proper monitoring network would enable us to minimize contamination or support emergency responses when accidents happen,” she says.

      Educating communities and students

      Part of empowering communities involves improving their ability to process science-based information. “Potentially hazardous facilities always end up in rural regions; minorities’ concerns are often ignored. The problem is that these regions don’t produce so many scientists or policymakers; they don’t have a voice,” Wainwright says, “I am determined to dedicate my time to improve STEM education in rural regions and to increase the voice in these regions.”

      In a project funded by DOE, she collaborates with the team of researchers at the University of Alaska — the Alaska Center for Energy and Power and Teaching Through Technology program — aiming to improve STEM education for rural and indigenous communities. “Alaska is an important place for energy transition and environmental justice,” Wainwright says. Micro-nuclear reactors can potentially improve the life of rural communities who bear the brunt of the high cost of fuel and transportation. However, there is a distrust of nuclear technologies, stemming from past nuclear weapon testing. At the same time, Alaska has vast metal mining resources for renewable energy and batteries. And there are concerns about environmental contamination from mining and various sources. The teams’ vision is much broader, she points out. “The focus is on broader environmental monitoring technologies and relevant STEM education, addressing general water and air qualities,” Wainwright says.

      The issues also weave into the courses Wainwright teaches at MIT. “I think it is important for engineering students to be aware of environmental justice related to energy waste and mining as well as past contamination events and their recovery,” she says. “It is not OK just to send waste to, or develop mines in, rural regions, which could be a special place for some people. We need to make sure that these developments will not harm the environment and health of local communities.” Wainwright also hopes that this knowledge will ultimately encourage students to think creatively about engineering designs that minimize waste or recycle material.

      The last question of the final quiz of one of her recent courses was: Assume that you store high-level radioactive waste in your “backyard.” What technical strategies would make you and your family feel safe? “All students thought about this question seriously and many suggested excellent points, including those addressing environmental monitoring,” Wainwright says, “that made me hopeful about the future.” More

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      in Data Management & Statistics

      Research, education, and connection in the face of war

      by

      When Russian forces invaded Ukraine in February 2022, Tetiana Herasymova had several decisions to make: What should she do, where should she live, and should she take her MITx MicroMasters capstone exams? She had registered for the Statistics and Data Science Program’s final exams just days prior to moving out of her apartment and into a bomb shelter. Although it was difficult to focus on studying and preparations with air horns sounding overhead and uncertainty lingering around her, she was determined to try. “I wouldn’t let the aggressor in the war squash my dreams,” she says.

      A love of research and the desire to improve teaching 

      An early love of solving puzzles and problems for fun piqued Herasymova’s initial interest in mathematics. When she later pursued her PhD in mathematics at Kiev National Taras Shevchenko University, Herasymova’s love of math evolved into a love of research. Throughout Herasymova’s career, she’s worked to close the gap between scientific researchers and educators. Starting as a math tutor at MBA Strategy, a company that prepares Ukrainian leaders for qualifying standardized tests for MBA programs, she was later promoted as the head of their test preparation department. Afterward, she moved on to an equivalent position at ZNOUA, a new project that prepared high school students for Ukraine’s standardized test, and she eventually became ZNOUA’s CEO.

      In 2018, she founded Prosteer, a “self-learning community” of educators who share research, pedagogy, and experience to learn from one another. “It’s really interesting to have a community of teachers from different domains,” she says, speaking of educators and researchers whose specialties range across language, mathematics, physics, music, and more.

      Implementing new pedagogical research in the classroom is often up to educators who seek out studies on an individual basis, Herasymova has found. “Lots of scientists are not practitioners,” she says, and the reverse is also true. She only became more determined to build these connections once she was promoted to head of test preparation at MBA Strategy because she wanted to share more effective pedagogy with the tutors she was mentoring.

      First, Herasymova knew she needed a way to measure the teachers’ effectiveness. She was able to determine whether students who received the company’s tutoring services improved their scores. Moreover, Ukraine keeps an open-access database of national standardized test scores, so anyone could analyze the data in hopes of improving the level of education in the country. She says, “I could do some analytics because I am a mathematician, but I knew I could do much more with this data if I knew data science and machine learning knowledge.”

      That’s why Herasymova sought out the MITx MicroMasters Program in Statistics and Data Science offered by the MIT Institute for Data, Systems, and Society (IDSS). “I wanted to learn the fundamentals so I could join the Learning Analytics domain,” she says. She was looking for a comprehensive program that covered the foundations without being overly basic. “I had some knowledge from the ground, so I could see the deepness of that course,” she says. Because of her background as an instructional designer, she thought the MicroMasters curriculum was well-constructed, calling the variety of videos, practice problems, and homework assignments that encouraged learners to approach the course material in different ways, “a perfect experience.”

      Another benefit of the MicroMasters program was its online format. “I had my usual work, so it was impossible to study in a stationary way,” she says. She found the structure to be more flexible than other programs. “It’s really great that you can construct your course schedule your own way, especially with your own adult life,” she says.

      Determination and support in the midst of war

      When the war first forced Herasymova to flee her apartment, she had already registered to take the exams for her four courses. “It was quite hard to prepare for exams when you could hear explosions outside of the bomb shelter,” she says. She and other Ukranians were invited to postpone their exams until the following session, but the next available testing period wouldn’t be held until October. “It was a hard decision, but I had to allow myself to try,” she says. “For all people in Ukraine, when you don’t know if you’re going to live or die, you try to live in the now. You have to appreciate every moment and what life brings to you. You don’t say, ‘Someday’ — you do it today or tomorrow.”

      In addition to emotional support from her boyfriend, Herasymova had a group of friends who had also enrolled in the program, and they supported each other through study sessions and an ongoing chat. Herasymova’s personal support network helped her accomplish what she set out to do with her MicroMasters program, and in turn, she was able to support her professional network. While Prosteer halted its regular work during the early stages of the war, Herasymova was determined to support the community of educators and scientists that she had built. They continued meeting weekly to exchange ideas as usual. “It’s intrinsic motivation,” she says. They managed to restore all of their activities by October.

      Despite the factors stacked against her, Herasymova’s determination paid off — she passed all of her exams in May, the final step to earning her MicroMasters certificate in statistics and data science. “I just couldn’t believe it,” she says. “It was definitely a bifurcation point. The moment when you realize that you have something to rely on, and that life is just beginning to show all its diversity despite the fact that you live in war.” With her newly minted certificate in hand, Herasymova has continued her research on the effectiveness of educational models — analyzing the data herself — with a summer research program at New York University. 

      The student becomes the master

      After moving seven times between February and October, heading west from Kyiv until most recently settling near the border of Poland, Herasymova hopes she’s moved for the last time. Ukrainian Catholic University offered her a position teaching both mathematics and programming. Before enrolling in the MicroMasters Program in Statistics and Data Science, she had some prior knowledge of programming languages and mathematical algorithms, but she didn’t know Python. She took MITx’s Introduction to Computer Science and Programming Using Python to prepare. “It gave me a huge step forward,” she says. “I learned a lot. Now, not only can I work with Python machine learning models in programming language R, I also have knowledge of the big picture of the purpose and the point to do so.”

      In addition to the skills the MicroMasters Program trained her in, she gained firsthand experience in learning new subjects and exploring topics more deeply. She will be sharing that practice with the community of students and teachers she’s built, plus, she plans on guiding them through this course during the next year. As a continuation of her own educational growth, says she’s looking forward to her next MITx course this year, Data Analysis.

      Herasymova advises that the best way to keep progressing is investing a lot of time. “Adults don’t want to hear this, but you need one or two years,” she says. “Allow yourself to be stupid. If you’re an expert in one domain and want to switch to another, or if you want to understand something new, a lot of people don’t ask questions or don’t ask for help. But from this point, if I don’t know something, I know I should ask for help because that’s the start of learning. With a fixed mindset, you won’t grow.”

      July 2022 MicroMasters Program Joint Completion Celebration. Ukrainian student Tetiana Herasymova, who completed her program amid war in her home country, speaks at 43:55. More