More stories

  • in

    Janabel Xia: Algorithms, dance rhythms, and the drive to succeed

    Senior math major Janabel Xia is a study of a person in constant motion.When she isn’t sorting algorithms and improving traffic control systems for driverless vehicles, she’s dancing as a member of at least four dance clubs. She’s joined several social justice organizations, worked on cryptography and web authentication technology, and created a polling app that allows users to vote anonymously.In her final semester, she’s putting the pedal to the metal, with a green light to lessen the carbon footprint of urban transportation by using sensors at traffic light intersections.First stepsGrowing up in Lexington, Massachusetts, Janabel has been competing on math teams since elementary school. On her math team, which met early mornings before the start of school, she discovered a love of problem-solving that challenged her more than her classroom “plug-and-chug exercises.”At Lexington High School, she was math team captain, a two-time Math Olympiad attendee, and a silver medalist for Team USA at the European Girls’ Mathematical Olympiad.As a math major, she studies combinatorics and theoretical computer science, including theoretical and applied cryptography. In her sophomore year, she was a researcher in the Cryptography and Information Security Group at the MIT Computer Science and Artificial Intelligence Laboratory, where she conducted cryptanalysis research under Professor Vinod Vaikuntanathan.Part of her interests in cryptography stem from the beauty of the underlying mathematics itself — the field feels like clever engineering with mathematical tools. But another part of her interest in cryptography stems from its political dimensions, including its potential to fundamentally change existing power structures and governance. Xia and students at the University of California at Berkeley and Stanford University created zkPoll, a private polling app written with the Circom programming language, that allows users to create polls for specific sets of people, while generating a zero-knowledge proof that keeps personal information hidden to decrease negative voting influences from public perception.Her participation in the PKG Center’s Active Community Engagement Freshman Pre-Orientation Program introduced her to local community organizations focusing on food security, housing for formerly incarcerated individuals, and access to health care. She is also part of Reading for Revolution, a student book club that discusses race, class, and working-class movements within MIT and the Greater Boston area.Xia’s educational journey led to her ongoing pursuit of combining mathematical and computational methods in areas adjacent to urban planning.  “When I realized how much planning was concerned with social justice as it was concerned with design, I became more attracted to the field.”Going on autopilotShe took classes with the Department of Urban Studies and Planning and is currently working on an Undergraduate Research Opportunities Program (UROP) project with Professor Cathy Wu in the Institute for Data, Systems, and Society.Recent work on eco-driving by Wu and doctoral student Vindula Jayawardana investigated semi-autonomous vehicles that communicate with sensors localized at traffic intersections, which in theory could reduce carbon emissions by up to 21 percent.Xia aims to optimize the implementation scheme for these sensors at traffic intersections, considering a graded scheme where perhaps only 20 percent of all sensors are initially installed, and more sensors get added in waves. She wants to maximize the emission reduction rates at each step of the process, as well as ensure there is no unnecessary installation and de-installation of such sensors.  Dance numbersMeanwhile, Xia has been a member of MIT’s Fixation, Ridonkulous, and MissBehavior groups, and as a traditional Chinese dance choreographer for the MIT Asian Dance Team. A dancer since she was 3, Xia started with Chinese traditional dance, and later added ballet and jazz. Because she is as much of a dancer as a researcher, she has figured out how to make her schedule work.“Production weeks are always madness, with dancers running straight from class to dress rehearsals and shows all evening and coming back early next morning to take down lights and roll up marley [material that covers the stage floor],” she says. “As busy as it keeps me, I couldn’t have survived MIT without dance. I love the discipline, creativity, and most importantly the teamwork that dance demands of us. I really love the dance community here with my whole heart. These friends have inspired me and given me the love to power me through MIT.”Xia lives with her fellow Dance Team members at the off-campus Women’s Independent Living Group (WILG).  “I really value WILG’s culture of independence, both in lifestyle — cooking, cleaning up after yourself, managing house facilities, etc. — and thought — questioning norms, staying away from status games, finding new passions.”In addition to her UROP, she’s wrapping up some graduation requirements, finishing up a research paper on sorting algorithms from her summer at the University of Minnesota Duluth Research Experience for Undergraduates in combinatorics, and deciding between PhD programs in math and computer science.  “My biggest goal right now is to figure out how to combine my interests in mathematics and urban studies, and more broadly connect technical perspectives with human-centered work in a way that feels right to me,” she says.“Overall, MIT has given me so many avenues to explore that I would have never thought about before coming here, for which I’m infinitely grateful. Every time I find something new, it’s hard for me not to find it cool. There’s just so much out there to learn about. While it can feel overwhelming at times, I hope to continue that learning and exploration for the rest of my life.” More

  • in

    Q&A: Exploring ethnic dynamics and climate change in Africa

    Evan Lieberman is the Total Professor of Political Science and Contemporary Africa at MIT, and is also director of the Center for International Studies. During a semester-long sabbatical, he’s currently based at the African Climate and Development Initiative at the University of Cape Town.In this Q&A, Lieberman discusses several climate-related research projects he’s pursuing in South Africa and surrounding countries. This is part of an ongoing series exploring how the School of Humanities, Arts, and Social Sciences is addressing the climate crisis.Q: South Africa is a nation whose political and economic development you have long studied and written about. Do you see this visit as an extension of the kind of research you have been pursuing, or a departure from it?A: Much of my previous work has been animated by the question of understanding the causes and consequences of group-based disparities, whether due to AIDS or Covid. These are problems that know no geographic boundaries, and where ethnic and racial minorities are often hardest hit. Climate change is an analogous problem, with these minority populations living in places where they are most vulnerable, in heat islands in cities, and in coastal areas where they are not protected. The reality is they might get hit much harder by longer-term trends and immediate shocks.In one line of research, I seek to understand how people in different African countries, in different ethnic groups, perceive the problems of climate change and their governments’ response to it. There are ethnic divisions of labor in terms of what people do — whether they are farmers or pastoralists, or live in cities. So some ethnic groups are simply more affected by drought or extreme weather than others, and this can be a basis for conflict, especially when competing for often limited government resources.In this area, just like in my previous research, learning what shapes ordinary citizen perspectives is really important, because these views affect people’s everyday practices, and the extent to which they support certain kinds of policies and investments their government makes in response to climate-related challenges. But I will also try to learn more about the perspectives of policymakers and various development partners who seek to balance climate-related challenges against a host of other problems and priorities.Q: You recently published “Until We Have Won Our Liberty,” which examines the difficult transition of South Africa from apartheid to a democratic government, scrutinizing in particular whether the quality of life for citizens has improved in terms of housing, employment, discrimination, and ethnic conflicts. How do climate change-linked issues fit into your scholarship?A: I never saw myself as a climate researcher, but a number of years ago, heavily influenced by what I was learning at MIT, I began to recognize more and more how important the issue of climate change is. And I realized there were lots of ways in which the climate problem resonated with other kinds of problems I had tackled in earlier parts of my work.There was once a time when climate and the environment was the purview primarily of white progressives: the “tree huggers.” And that’s really changed in recent decades as it has become evident that the people who’ve been most affected by the climate emergency are ethnic and racial minorities. We saw with Hurricane Katrina and other places [that] if you are Black, you’re more likely to live in a vulnerable area and to just generally experience more environmental harms, from pollution and emissions, leaving these communities much less resilient than white communities. Government has largely not addressed this inequity. When you look at American survey data in terms of who’s concerned about climate change, Black Americans, Hispanic Americans, and Asian Americans are more unified in their worries than are white Americans.There are analogous problems in Africa, my career research focus. Governments there have long responded in different ways to different ethnic groups. The research I am starting looks at the extent to which there are disparities in how governments try to solve climate-related challenges.Q: It’s difficult enough in the United States taking the measure of different groups’ perceptions of the impact of climate change and government’s effectiveness in contending with it. How do you go about this in Africa?A: Surprisingly, there’s only been a little bit of work done so far on how ordinary African citizens, who are ostensibly being hit the hardest in the world by the climate emergency, are thinking about this problem. Climate change has not been politicized there in a very big way. In fact, only 50 percent of Africans in one poll had heard of the term.In one of my new projects, with political science faculty colleague Devin Caughey and political science doctoral student Preston Johnston, we are analyzing social and climate survey data [generated by the Afrobarometer research network] from over 30 African countries to understand within and across countries the ways in which ethnic identities structure people’s perception of the climate crisis, and their beliefs in what government ought to be doing. In largely agricultural African societies, people routinely experience drought, extreme rain, and heat. They also lack the infrastructure that can shield them from the intense variability of weather patterns. But we’re adding a lens, which is looking at sources of inequality, especially ethnic differences.I will also be investigating specific sectors. Africa is a continent where in most places people cannot take for granted universal, piped access to clean water. In Cape Town, several years ago, the combination of failure to replace infrastructure and lack of rain caused such extreme conditions that one of the world’s most important cities almost ran out of water.While these studies are in progress, it is clear that in many countries, there are substantively large differences in perceptions of the severity of climate change, and attitudes about who should be doing what, and who’s capable of doing what. In several countries, both perceptions and policy preferences are differentiated along ethnic lines, more so than with respect to generational or class differences within societies.This is interesting as a phenomenon, but substantively, I think it’s important in that it may provide the basis for how politicians and government actors decide to move on allocating resources and implementing climate-protection policies. We see this kind of political calculation in the U.S. and we shouldn’t be surprised that it happens in Africa as well.That’s ultimately one of the challenges from the perch of MIT, where we’re really interested in understanding climate change, and creating technological tools and policies for mitigating the problem or adapting to it. The reality is frustrating. The political world — those who make decisions about whether to acknowledge the problem and whether to implement resources in the best technical way — are playing a whole other game. That game is about rewarding key supporters and being reelected.Q: So how do you go from measuring perceptions and beliefs among citizens about climate change and government responsiveness to those problems, to policies and actions that might actually reduce disparities in the way climate-vulnerable African groups receive support?A: Some of the work I have been doing involves understanding what local and national governments across Africa are actually doing to address these problems. We will have to drill down into government budgets to determine the actual resources devoted to addressing a challenge, what sorts of practices the government follows, and the political ramifications for governments that act aggressively versus those that don’t. With the Cape Town water crisis, for example, the government dramatically changed residents’ water usage through naming and shaming, and transformed institutional practices of water collection. They made it through a major drought by using much less water, and doing it with greater energy efficiency. Through the government’s strong policy and implementation, and citizens’ active responses, an entire city, with all its disparate groups, gained resilience. Maybe we can highlight creative solutions to major climate-related problems and use them as prods to push more effective policies and solutions in other places.In the MIT Global Diversity Lab, along with political science faculty colleague Volha Charnysh, political science doctoral student Jared Kalow, and Institute for Data, Systems and Society doctoral student Erin Walk, we are exploring American perspectives on climate-related foreign aid, asking survey respondents whether the U.S. should be giving more to people in the global South who didn’t cause the problems of climate change but have to suffer the externalities. We are particularly interested in whether people’s desire to help vulnerable communities rests on the racial or national identity of those communities.From my new seat as director of the Center for International Studies (CIS), I hope to do more and more to connect social science findings to relevant policymakers, whether in the U.S. or in other places. CIS is making climate one of our thematic priority areas, directing hundreds of thousands of dollars for MIT faculty to spark climate collaborations with researchers worldwide through the Global Seed Fund program. COP 28 (the U.N. Climate Change Conference), which I attended in December in Dubai, really drove home the importance of people coming together from around the world to exchange ideas and form networks. It was unbelievably large, with 85,000 people. But so many of us shared the belief that we are not doing enough. We need enforceable global solutions and innovation. We need ways of financing. We need to provide opportunities for journalists to broadcast the importance of this problem. And we need to understand the incentives that different actors have and what sorts of messages and strategies will resonate with them, and inspire those who have resources to be more generous. More

  • in

    Growing our donated organ supply

    For those in need of one, an organ transplant is a matter of life and death. 

    Every year, the medical procedure gives thousands of people with advanced or end-stage diseases extended life. This “second chance” is heavily dependent on the availability, compatibility, and proximity of a precious resource that can’t be simply bought, grown, or manufactured — at least not yet.

    Instead, organs must be given — cut from one body and implanted into another. And because living organ donation is only viable in certain cases, many organs are only available for donation after the donor’s death.

    Unsurprisingly, the logistical and ethical complexity of distributing a limited number of transplant organs to a growing wait list of patients has received much attention. There’s an important part of the process that has received less focus, however, and which may hold significant untapped potential: organ procurement itself.

    “If you have a donated organ, who should you give it to? This question has been extensively studied in operations research, economics, and even applied computer science,” says Hammaad Adam, a graduate student in the Social and Engineering Systems (SES) doctoral program at the MIT Institute for Data, Systems, and Society (IDSS). “But there’s been a lot less research on where that organ comes from in the first place.”

    In the United States, nonprofits called organ procurement organizations, or OPOs, are responsible for finding and evaluating potential donors, interacting with grieving families and hospital administrations, and recovering and delivering organs — all while following the federal laws that serve as both their mandate and guardrails. Recent studies estimate that obstacles and inefficiencies lead to thousands of organs going uncollected every year, even as the demand for transplants continues to grow.

    “There’s been little transparent data on organ procurement,” argues Adam. Working with MIT computer science professors Marzyeh Ghassemi and Ashia Wilson, and in collaboration with stakeholders in organ procurement, Adam led a project to create a dataset called ORCHID: Organ Retrieval and Collection of Health Information for Donation. ORCHID contains a decade of clinical, financial, and administrative data from six OPOs.

    “Our goal is for the ORCHID database to have an impact in how organ procurement is understood, internally and externally,” says Ghassemi.

    Efficiency and equity 

    It was looking to make an impact that drew Adam to SES and MIT. With a background in applied math and experience in strategy consulting, solving problems with technical components sits right in his wheelhouse.

    “I really missed challenging technical problems from a statistics and machine learning standpoint,” he says of his time in consulting. “So I went back and got a master’s in data science, and over the course of my master’s got involved in a bunch of academic research projects in a few different fields, including biology, management science, and public policy. What I enjoyed most were some of the more social science-focused projects that had immediate impact.”

    As a grad student in SES, Adam’s research focuses on using statistical tools to uncover health-care inequities, and developing machine learning approaches to address them. “Part of my dissertation research focuses on building tools that can improve equity in clinical trials and other randomized experiments,” he explains.

    One recent example of Adam’s work: developing a novel method to stop clinical trials early if the treatment has an unintended harmful effect for a minority group of participants. “I’ve also been thinking about ways to increase minority representation in clinical trials through improved patient recruitment,” he adds.

    Racial inequities in health care extend into organ transplantation, where a majority of wait-listed patients are not white — far in excess of their demographic groups’ proportion to the overall population. There are fewer organ donations from many of these communities, due to various obstacles in need of better understanding if they are to be overcome. 

    “My work in organ transplantation began on the allocation side,” explains Adam. “In work under review, we examined the role of race in the acceptance of heart, liver, and lung transplant offers by physicians on behalf of their patients. We found that Black race of the patient was associated with significantly lower odds of organ offer acceptance — in other words, transplant doctors seemed more likely to turn down organs offered to Black patients. This trend may have multiple explanations, but it is nevertheless concerning.”

    Adam’s research has also found that donor-candidate race match was associated with significantly higher odds of offer acceptance, an association that Adam says “highlights the importance of organ donation from racial minority communities, and has motivated our work on equitable organ procurement.”

    Working with Ghassemi through the IDSS Initiative on Combatting Systemic Racism, Adam was introduced to OPO stakeholders looking to collaborate. “It’s this opportunity to impact not only health-care efficiency, but also health-care equity, that really got me interested in this research,” says Adam.

    Play video

    MIT Initiative on Combatting Systemic Racism – HealthcareVideo: IDSS

    Making an impact

    Creating a database like ORCHID means solving problems in multiple domains, from the technical to the political. Some efforts never overcome the first step: getting data in the first place. Thankfully, several OPOs were already seeking collaborations and looking to improve their performance.

    “We have been lucky to have a strong partnership with the OPOs, and we hope to work together to find important insights to improve efficiency and equity,” says Ghassemi.

    The value of a database like ORCHID is in its potential for generating new insights, especially through quantitative analysis with statistics and computing tools like machine learning. The potential value in ORCHID was recognized with an MIT Prize for Open Data, an MIT Libraries award highlighting the importance and impact of research data that is openly shared.

    “It’s nice that the work got some recognition,” says Adam of the prize. “And it was cool to see some of the other great open data work that’s happening at MIT. I think there’s real impact in releasing publicly available data in an important and understudied domain.”

    All the same, Adam knows that building the database is only the first step.

    “I’m very interested in understanding the bottlenecks in the organ procurement process,” he explains. “As part of my thesis research, I’m exploring this by modeling OPO decision-making using causal inference and structural econometrics.”

    Using insights from this research, Adam also aims to evaluate policy changes that can improve both equity and efficiency in organ procurement. “And we’re hoping to recruit more OPOs, and increase the amount of data we’re releasing,” he says. “The dream state is every OPO joins our collaboration and provides updated data every year.”

    Adam is excited to see how other researchers might use the data to address inefficiencies in organ procurement. “Every organ donor saves between three and four lives,” he says. “So every research project that comes out of this dataset could make a real impact.” More

  • in

    Characterizing social networks

    People tend to connect with others who are like them. Alumni from the same alma mater are more likely to collaborate over a research project together, or individuals with the same political beliefs are more likely to join the same political parties, attend rallies, and engage in online discussions. This sociology concept, called homophily, has been observed in many network science studies. But if like-minded individuals cluster in online and offline spaces to reinforce each other’s ideas and form synergies, what does that mean for society?

    Researchers at MIT wanted to investigate homophily further to understand how groups of three or more interact in complex societal settings. Prior research on understanding homophily has studied relationships between pairs of people. For example, when two members of Congress co-sponsor a bill, they are likely to be from the same political party.

    However, less is known about whether group interactions between three or more people are likely to occur between similar individuals. If three members of Congress co-sponsor a bill together, are all three likely to be members of the same party, or would we expect more bipartisanship? When the researchers tried to extend traditional methods to measure homophily in these larger group interactions, they found the results can be misleading.

    “We found that homophily observed in pairs, or one-to-one interactions, can make it seem like there’s more homophily in larger groups than there really is,” says Arnab Sarker, graduate student in the Institute for Data, Systems and Society (IDSS) and lead author of the study published in Proceedings of the National Academy of Sciences. “The previous measure didn’t account for the way in which two people already know each other in friendship settings,” he adds.

    To address this issue, Sarker, along with co-authors Natalie Northrup ’22 and Ali Jadbabaie, the JR East Professor of Engineering, head of the Department of Civil and Environmental Engineering, and core faculty member of IDSS, developed a new way of measuring homophily. Borrowing tools from algebraic topology, a subfield in mathematics typically applied in physics, they developed a new measure to understand whether homophily occurred in group interactions.

    The new measure, called simplicial homophily, separates the homophily seen in one-on-one interactions from those in larger group interactions and is based on the mathematical concept of a simplicial complex. The researchers tested this new measure with real-world data from 16 different datasets and found that simplicial homophily provides more accurate insights into how similar things interact in larger groups. Interestingly, the new measure can better identify instances where there is a lack of similarity in larger group interactions, thus rectifying a weakness observed in the previous measure.

    One such example of this instance was demonstrated in the dataset from the global hotel booking website, Trivago. They found that when travelers are looking at two hotels in one session, they often pick hotels that are close to one another geographically. But when they look at more than two hotels in one session, they are more likely to be searching for hotels that are farther apart from one another (for example, if they are taking a vacation with multiple stops). The new method showed “anti-homophily” — instead of similar hotels being chosen together, different hotels were chosen together.

    “Our measure controls for pairwise connections and is suggesting that there’s more diversity in the hotels that people are looking for as group size increases, which is an interesting economic result,” says Sarker.

    Additionally, they discovered that simplicial homophily can help identify when certain characteristics are important for predicting if groups will interact in the future. They found that when there’s a lot of similarity or a lot of difference between individuals who already interact in groups, then knowing individual characteristics can help predict their connection to each other in the future.

    Northrup was an undergraduate researcher on the project and worked with Sarker and Jadbabaie over three semesters before she graduated. The project gave her an opportunity to take some of the concepts she learned in the classroom and apply them.

    “Working on this project, I really dove into building out the higher-order network model, and understanding the network, the math, and being able to implement it at a large scale,” says Northrup, who was in the civil and environmental engineering systems track with a double major in economics.

    The new measure opens up opportunities to study complex group interactions in a broad range of network applications, from ecology to traffic and socioeconomics. One of the areas Sarker has interest in exploring is the group dynamics of people finding jobs through social networks. “Does higher-order homophily affect how people get information about jobs?” he asks.    

    Northrup adds that it could also be used to evaluate interventions or specific policies to connect people with job opportunities outside of their network. “You can even use it as a measurement to evaluate how effective that might be.”

    The research was supported through funding from a Vannevar Bush Fellowship from the Office of the U.S. Secretary of Defense and from the U.S. Army Research Office Multidisciplinary University Research Initiative. More

  • in

    Dealing with the limitations of our noisy world

    Tamara Broderick first set foot on MIT’s campus when she was a high school student, as a participant in the inaugural Women’s Technology Program. The monthlong summer academic experience gives young women a hands-on introduction to engineering and computer science.

    What is the probability that she would return to MIT years later, this time as a faculty member?

    That’s a question Broderick could probably answer quantitatively using Bayesian inference, a statistical approach to probability that tries to quantify uncertainty by continuously updating one’s assumptions as new data are obtained.

    In her lab at MIT, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS) uses Bayesian inference to quantify uncertainty and measure the robustness of data analysis techniques.

    “I’ve always been really interested in understanding not just ‘What do we know from data analysis,’ but ‘How well do we know it?’” says Broderick, who is also a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society. “The reality is that we live in a noisy world, and we can’t always get exactly the data that we want. How do we learn from data but at the same time recognize that there are limitations and deal appropriately with them?”

    Broadly, her focus is on helping people understand the confines of the statistical tools available to them and, sometimes, working with them to craft better tools for a particular situation.

    For instance, her group recently collaborated with oceanographers to develop a machine-learning model that can make more accurate predictions about ocean currents. In another project, she and others worked with degenerative disease specialists on a tool that helps severely motor-impaired individuals utilize a computer’s graphical user interface by manipulating a single switch.

    A common thread woven through her work is an emphasis on collaboration.

    “Working in data analysis, you get to hang out in everybody’s backyard, so to speak. You really can’t get bored because you can always be learning about some other field and thinking about how we can apply machine learning there,” she says.

    Hanging out in many academic “backyards” is especially appealing to Broderick, who struggled even from a young age to narrow down her interests.

    A math mindset

    Growing up in a suburb of Cleveland, Ohio, Broderick had an interest in math for as long as she can remember. She recalls being fascinated by the idea of what would happen if you kept adding a number to itself, starting with 1+1=2 and then 2+2=4.

    “I was maybe 5 years old, so I didn’t know what ‘powers of two’ were or anything like that. I was just really into math,” she says.

    Her father recognized her interest in the subject and enrolled her in a Johns Hopkins program called the Center for Talented Youth, which gave Broderick the opportunity to take three-week summer classes on a range of subjects, from astronomy to number theory to computer science.

    Later, in high school, she conducted astrophysics research with a postdoc at Case Western University. In the summer of 2002, she spent four weeks at MIT as a member of the first class of the Women’s Technology Program.

    She especially enjoyed the freedom offered by the program, and its focus on using intuition and ingenuity to achieve high-level goals. For instance, the cohort was tasked with building a device with LEGOs that they could use to biopsy a grape suspended in Jell-O.

    The program showed her how much creativity is involved in engineering and computer science, and piqued her interest in pursuing an academic career.

    “But when I got into college at Princeton, I could not decide — math, physics, computer science — they all seemed super-cool. I wanted to do all of it,” she says.

    She settled on pursuing an undergraduate math degree but took all the physics and computer science courses she could cram into her schedule.

    Digging into data analysis

    After receiving a Marshall Scholarship, Broderick spent two years at Cambridge University in the United Kingdom, earning a master of advanced study in mathematics and a master of philosophy in physics.

    In the UK, she took a number of statistics and data analysis classes, including her first class on Bayesian data analysis in the field of machine learning.

    It was a transformative experience, she recalls.

    “During my time in the U.K., I realized that I really like solving real-world problems that matter to people, and Bayesian inference was being used in some of the most important problems out there,” she says.

    Back in the U.S., Broderick headed to the University of California at Berkeley, where she joined the lab of Professor Michael I. Jordan as a grad student. She earned a PhD in statistics with a focus on Bayesian data analysis. 

    She decided to pursue a career in academia and was drawn to MIT by the collaborative nature of the EECS department and by how passionate and friendly her would-be colleagues were.

    Her first impressions panned out, and Broderick says she has found a community at MIT that helps her be creative and explore hard, impactful problems with wide-ranging applications.

    “I’ve been lucky to work with a really amazing set of students and postdocs in my lab — brilliant and hard-working people whose hearts are in the right place,” she says.

    One of her team’s recent projects involves a collaboration with an economist who studies the use of microcredit, or the lending of small amounts of money at very low interest rates, in impoverished areas.

    The goal of microcredit programs is to raise people out of poverty. Economists run randomized control trials of villages in a region that receive or don’t receive microcredit. They want to generalize the study results, predicting the expected outcome if one applies microcredit to other villages outside of their study.

    But Broderick and her collaborators have found that results of some microcredit studies can be very brittle. Removing one or a few data points from the dataset can completely change the results. One issue is that researchers often use empirical averages, where a few very high or low data points can skew the results.

    Using machine learning, she and her collaborators developed a method that can determine how many data points must be dropped to change the substantive conclusion of the study. With their tool, a scientist can see how brittle the results are.

    “Sometimes dropping a very small fraction of data can change the major results of a data analysis, and then we might worry how far those conclusions generalize to new scenarios. Are there ways we can flag that for people? That is what we are getting at with this work,” she explains.

    At the same time, she is continuing to collaborate with researchers in a range of fields, such as genetics, to understand the pros and cons of different machine-learning techniques and other data analysis tools.

    Happy trails

    Exploration is what drives Broderick as a researcher, and it also fuels one of her passions outside the lab. She and her husband enjoy collecting patches they earn by hiking all the trails in a park or trail system.

    “I think my hobby really combines my interests of being outdoors and spreadsheets,” she says. “With these hiking patches, you have to explore everything and then you see areas you wouldn’t normally see. It is adventurous, in that way.”

    They’ve discovered some amazing hikes they would never have known about, but also embarked on more than a few “total disaster hikes,” she says. But each hike, whether a hidden gem or an overgrown mess, offers its own rewards.

    And just like in her research, curiosity, open-mindedness, and a passion for problem-solving have never led her astray. More

  • in

    Power when the sun doesn’t shine

    In 2016, at the huge Houston energy conference CERAWeek, MIT materials scientist Yet-Ming Chiang found himself talking to a Tesla executive about a thorny problem: how to store the output of solar panels and wind turbines for long durations.        

    Chiang, the Kyocera Professor of Materials Science and Engineering, and Mateo Jaramillo, a vice president at Tesla, knew that utilities lacked a cost-effective way to store renewable energy to cover peak levels of demand and to bridge the gaps during windless and cloudy days. They also knew that the scarcity of raw materials used in conventional energy storage devices needed to be addressed if renewables were ever going to displace fossil fuels on the grid at scale.

    Energy storage technologies can facilitate access to renewable energy sources, boost the stability and reliability of power grids, and ultimately accelerate grid decarbonization. The global market for these systems — essentially large batteries — is expected to grow tremendously in the coming years. A study by the nonprofit LDES (Long Duration Energy Storage) Council pegs the long-duration energy storage market at between 80 and 140 terawatt-hours by 2040. “That’s a really big number,” Chiang notes. “Every 10 people on the planet will need access to the equivalent of one EV [electric vehicle] battery to support their energy needs.”

    In 2017, one year after they met in Houston, Chiang and Jaramillo joined forces to co-found Form Energy in Somerville, Massachusetts, with MIT graduates Marco Ferrara SM ’06, PhD ’08 and William Woodford PhD ’13, and energy storage veteran Ted Wiley.

    “There is a burgeoning market for electrical energy storage because we want to achieve decarbonization as fast and as cost-effectively as possible,” says Ferrara, Form’s senior vice president in charge of software and analytics.

    Investors agreed. Over the next six years, Form Energy would raise more than $800 million in venture capital.

    Bridging gaps

    The simplest battery consists of an anode, a cathode, and an electrolyte. During discharge, with the help of the electrolyte, electrons flow from the negative anode to the positive cathode. During charge, external voltage reverses the process. The anode becomes the positive terminal, the cathode becomes the negative terminal, and electrons move back to where they started. Materials used for the anode, cathode, and electrolyte determine the battery’s weight, power, and cost “entitlement,” which is the total cost at the component level.

    During the 1980s and 1990s, the use of lithium revolutionized batteries, making them smaller, lighter, and able to hold a charge for longer. The storage devices Form Energy has devised are rechargeable batteries based on iron, which has several advantages over lithium. A big one is cost.

    Chiang once declared to the MIT Club of Northern California, “I love lithium-ion.” Two of the four MIT spinoffs Chiang founded center on innovative lithium-ion batteries. But at hundreds of dollars a kilowatt-hour (kWh) and with a storage capacity typically measured in hours, lithium-ion was ill-suited for the use he now had in mind.

    The approach Chiang envisioned had to be cost-effective enough to boost the attractiveness of renewables. Making solar and wind energy reliable enough for millions of customers meant storing it long enough to fill the gaps created by extreme weather conditions, grid outages, and when there is a lull in the wind or a few days of clouds.

    To be competitive with legacy power plants, Chiang’s method had to come in at around $20 per kilowatt-hour of stored energy — one-tenth the cost of lithium-ion battery storage.

    But how to transition from expensive batteries that store and discharge over a couple of hours to some as-yet-undefined, cheap, longer-duration technology?

    “One big ball of iron”

    That’s where Ferrara comes in. Ferrara has a PhD in nuclear engineering from MIT and a PhD in electrical engineering and computer science from the University of L’Aquila in his native Italy. In 2017, as a research affiliate at the MIT Department of Materials Science and Engineering, he worked with Chiang to model the grid’s need to manage renewables’ intermittency.

    How intermittent depends on where you are. In the United States, for instance, there’s the windy Great Plains; the sun-drenched, relatively low-wind deserts of Arizona, New Mexico, and Nevada; and the often-cloudy Pacific Northwest.

    Ferrara, in collaboration with Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society and her MIT team, modeled four representative locations in the United States and concluded that energy storage with capacity costs below roughly $20/kWh and discharge durations of multiple days would allow a wind-solar mix to provide cost-competitive, firm electricity in resource-abundant locations.

    Now that they had a time frame, they turned their attention to materials. At the price point Form Energy was aiming for, lithium was out of the question. Chiang looked at plentiful and cheap sulfur. But a sulfur, sodium, water, and air battery had technical challenges.

    Thomas Edison once used iron as an electrode, and iron-air batteries were first studied in the 1960s. They were too heavy to make good transportation batteries. But this time, Chiang and team were looking at a battery that sat on the ground, so weight didn’t matter. Their priorities were cost and availability.

    “Iron is produced, mined, and processed on every continent,” Chiang says. “The Earth is one big ball of iron. We wouldn’t ever have to worry about even the most ambitious projections of how much storage that the world might use by mid-century.” If Form ever moves into the residential market, “it’ll be the safest battery you’ve ever parked at your house,” Chiang laughs. “Just iron, air, and water.”

    Scientists call it reversible rusting. While discharging, the battery takes in oxygen and converts iron to rust. Applying an electrical current converts the rusty pellets back to iron, and the battery “breathes out” oxygen as it charges. “In chemical terms, you have iron, and it becomes iron hydroxide,” Chiang says. “That means electrons were extracted. You get those electrons to go through the external circuit, and now you have a battery.”

    Form Energy’s battery modules are approximately the size of a washer-and-dryer unit. They are stacked in 40-foot containers, and several containers are electrically connected with power conversion systems to build storage plants that can cover several acres.

    The right place at the right time

    The modules don’t look or act like anything utilities have contracted for before.

    That’s one of Form’s key challenges. “There is not widespread knowledge of needing these new tools for decarbonized grids,” Ferrara says. “That’s not the way utilities have typically planned. They’re looking at all the tools in the toolkit that exist today, which may not contemplate a multi-day energy storage asset.”

    Form Energy’s customers are largely traditional power companies seeking to expand their portfolios of renewable electricity. Some are in the process of decommissioning coal plants and shifting to renewables.

    Ferrara’s research pinpointing the need for very low-cost multi-day storage provides key data for power suppliers seeking to determine the most cost-effective way to integrate more renewable energy.

    Using the same modeling techniques, Ferrara and team show potential customers how the technology fits in with their existing system, how it competes with other technologies, and how, in some cases, it can operate synergistically with other storage technologies.

    “They may need a portfolio of storage technologies to fully balance renewables on different timescales of intermittency,” he says. But other than the technology developed at Form, “there isn’t much out there, certainly not within the cost entitlement of what we’re bringing to market.”  Thanks to Chiang and Jaramillo’s chance encounter in Houston, Form has a several-year lead on other companies working to address this challenge. 

    In June 2023, Form Energy closed its biggest deal to date for a single project: Georgia Power’s order for a 15-megawatt/1,500-megawatt-hour system. That order brings Form’s total amount of energy storage under contracts with utility customers to 40 megawatts/4 gigawatt-hours. To meet the demand, Form is building a new commercial-scale battery manufacturing facility in West Virginia.

    The fact that Form Energy is creating jobs in an area that lost more than 10,000 steel jobs over the past decade is not lost on Chiang. “And these new jobs are in clean tech. It’s super exciting to me personally to be doing something that benefits communities outside of our traditional technology centers.

    “This is the right time for so many reasons,” Chiang says. He says he and his Form Energy co-founders feel “tremendous urgency to get these batteries out into the world.”

    This article appears in the Winter 2024 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

  • in

    New AI model could streamline operations in a robotic warehouse

    Hundreds of robots zip back and forth across the floor of a colossal robotic warehouse, grabbing items and delivering them to human workers for packing and shipping. Such warehouses are increasingly becoming part of the supply chain in many industries, from e-commerce to automotive production.

    However, getting 800 robots to and from their destinations efficiently while keeping them from crashing into each other is no easy task. It is such a complex problem that even the best path-finding algorithms struggle to keep up with the breakneck pace of e-commerce or manufacturing. 

    In a sense, these robots are like cars trying to navigate a crowded city center. So, a group of MIT researchers who use AI to mitigate traffic congestion applied ideas from that domain to tackle this problem.

    They built a deep-learning model that encodes important information about the warehouse, including the robots, planned paths, tasks, and obstacles, and uses it to predict the best areas of the warehouse to decongest to improve overall efficiency.

    Their technique divides the warehouse robots into groups, so these smaller groups of robots can be decongested faster with traditional algorithms used to coordinate robots. In the end, their method decongests the robots nearly four times faster than a strong random search method.

    In addition to streamlining warehouse operations, this deep learning approach could be used in other complex planning tasks, like computer chip design or pipe routing in large buildings.

    “We devised a new neural network architecture that is actually suitable for real-time operations at the scale and complexity of these warehouses. It can encode hundreds of robots in terms of their trajectories, origins, destinations, and relationships with other robots, and it can do this in an efficient manner that reuses computation across groups of robots,” says Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

    Wu, senior author of a paper on this technique, is joined by lead author Zhongxia Yan, a graduate student in electrical engineering and computer science. The work will be presented at the International Conference on Learning Representations.

    Robotic Tetris

    From a bird’s eye view, the floor of a robotic e-commerce warehouse looks a bit like a fast-paced game of “Tetris.”

    When a customer order comes in, a robot travels to an area of the warehouse, grabs the shelf that holds the requested item, and delivers it to a human operator who picks and packs the item. Hundreds of robots do this simultaneously, and if two robots’ paths conflict as they cross the massive warehouse, they might crash.

    Traditional search-based algorithms avoid potential crashes by keeping one robot on its course and replanning a trajectory for the other. But with so many robots and potential collisions, the problem quickly grows exponentially.

    “Because the warehouse is operating online, the robots are replanned about every 100 milliseconds. That means that every second, a robot is replanned 10 times. So, these operations need to be very fast,” Wu says.

    Because time is so critical during replanning, the MIT researchers use machine learning to focus the replanning on the most actionable areas of congestion — where there exists the most potential to reduce the total travel time of robots.

    Wu and Yan built a neural network architecture that considers smaller groups of robots at the same time. For instance, in a warehouse with 800 robots, the network might cut the warehouse floor into smaller groups that contain 40 robots each.

    Then, it predicts which group has the most potential to improve the overall solution if a search-based solver were used to coordinate trajectories of robots in that group.

    An iterative process, the overall algorithm picks the most promising robot group with the neural network, decongests the group with the search-based solver, then picks the next most promising group with the neural network, and so on.

    Considering relationships

    The neural network can reason about groups of robots efficiently because it captures complicated relationships that exist between individual robots. For example, even though one robot may be far away from another initially, their paths could still cross during their trips.

    The technique also streamlines computation by encoding constraints only once, rather than repeating the process for each subproblem. For instance, in a warehouse with 800 robots, decongesting a group of 40 robots requires holding the other 760 robots as constraints. Other approaches require reasoning about all 800 robots once per group in each iteration.

    Instead, the researchers’ approach only requires reasoning about the 800 robots once across all groups in each iteration.

    “The warehouse is one big setting, so a lot of these robot groups will have some shared aspects of the larger problem. We designed our architecture to make use of this common information,” she adds.

    They tested their technique in several simulated environments, including some set up like warehouses, some with random obstacles, and even maze-like settings that emulate building interiors.

    By identifying more effective groups to decongest, their learning-based approach decongests the warehouse up to four times faster than strong, non-learning-based approaches. Even when they factored in the additional computational overhead of running the neural network, their approach still solved the problem 3.5 times faster.

    In the future, the researchers want to derive simple, rule-based insights from their neural model, since the decisions of the neural network can be opaque and difficult to interpret. Simpler, rule-based methods could also be easier to implement and maintain in actual robotic warehouse settings.

    “This approach is based on a novel architecture where convolution and attention mechanisms interact effectively and efficiently. Impressively, this leads to being able to take into account the spatiotemporal component of the constructed paths without the need of problem-specific feature engineering. The results are outstanding: Not only is it possible to improve on state-of-the-art large neighborhood search methods in terms of quality of the solution and speed, but the model generalizes to unseen cases wonderfully,” says Andrea Lodi, the Andrew H. and Ann R. Tisch Professor at Cornell Tech, and who was not involved with this research.

    This work was supported by Amazon and the MIT Amazon Science Hub. More

  • in

    Automated method helps researchers quantify uncertainty in their predictions

    Pollsters trying to predict presidential election results and physicists searching for distant exoplanets have at least one thing in common: They often use a tried-and-true scientific technique called Bayesian inference.

    Bayesian inference allows these scientists to effectively estimate some unknown parameter — like the winner of an election — from data such as poll results. But Bayesian inference can be slow, sometimes consuming weeks or even months of computation time or requiring a researcher to spend hours deriving tedious equations by hand. 

    Researchers from MIT and elsewhere have introduced an optimization technique that speeds things up without requiring a scientist to do a lot of additional work. Their method can achieve more accurate results faster than another popular approach for accelerating Bayesian inference.

    Using this new automated technique, a scientist could simply input their model and then the optimization method does all the calculations under the hood to provide an approximation of some unknown parameter. The method also offers reliable uncertainty estimates that can help a researcher understand when to trust its predictions.

    This versatile technique could be applied to a wide array of scientific quandaries that incorporate Bayesian inference. For instance, it could be used by economists studying the impact of microcredit loans in developing nations or sports analysts using a model to rank top tennis players.

    “When you actually dig into what people are doing in the social sciences, physics, chemistry, or biology, they are often using a lot of the same tools under the hood. There are so many Bayesian analyses out there. If we can build a really great tool that makes these researchers lives easier, then we can really make a difference to a lot of people in many different research areas,” says senior author Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.

    Broderick is joined on the paper by co-lead authors Ryan Giordano, an assistant professor of statistics at the University of California at Berkeley; and Martin Ingram, a data scientist at the AI company KONUX. The paper was recently published in the Journal of Machine Learning Research.

    Faster results

    When researchers seek a faster form of Bayesian inference, they often turn to a technique called automatic differentiation variational inference (ADVI), which is often both fast to run and easy to use.

    But Broderick and her collaborators have found a number of practical issues with ADVI. It has to solve an optimization problem and can do so only approximately. So, ADVI can still require a lot of computation time and user effort to determine whether the approximate solution is good enough. And once it arrives at a solution, it tends to provide poor uncertainty estimates.

    Rather than reinventing the wheel, the team took many ideas from ADVI but turned them around to create a technique called deterministic ADVI (DADVI) that doesn’t have these downsides.

    With DADVI, it is very clear when the optimization is finished, so a user won’t need to spend extra computation time to ensure that the best solution has been found. DADVI also permits the incorporation of more powerful optimization methods that give it an additional speed and performance boost.

    Once it reaches a result, DADVI is set up to allow the use of uncertainty corrections. These corrections make its uncertainty estimates much more accurate than those of ADVI.

    DADVI also enables the user to clearly see how much error they have incurred in the approximation to the optimization problem. This prevents a user from needlessly running the optimization again and again with more and more resources to try and reduce the error.

    “We wanted to see if we could live up to the promise of black-box inference in the sense of, once the user makes their model, they can just run Bayesian inference and don’t have to derive everything by hand, they don’t need to figure out when to stop their algorithm, and they have a sense of how accurate their approximate solution is,” Broderick says.

    Defying conventional wisdom

    DADVI can be more effective than ADVI because it uses an efficient approximation method, called sample average approximation, which estimates an unknown quantity by taking a series of exact steps.

    Because the steps along the way are exact, it is clear when the objective has been reached. Plus, getting to that objective typically requires fewer steps.

    Often, researchers expect sample average approximation to be more computationally intensive than a more popular method, known as stochastic gradient, which is used by ADVI. But Broderick and her collaborators showed that, in many applications, this is not the case.

    “A lot of problems really do have special structure, and you can be so much more efficient and get better performance by taking advantage of that special structure. That is something we have really seen in this paper,” she adds.

    They tested DADVI on a number of real-world models and datasets, including a model used by economists to evaluate the effectiveness of microcredit loans and one used in ecology to determine whether a species is present at a particular site.

    Across the board, they found that DADVI can estimate unknown parameters faster and more reliably than other methods, and achieves as good or better accuracy than ADVI. Because it is easier to use than other techniques, DADVI could offer a boost to scientists in a wide variety of fields.

    In the future, the researchers want to dig deeper into correction methods for uncertainty estimates so they can better understand why these corrections can produce such accurate uncertainties, and when they could fall short.

    “In applied statistics, we often have to use approximate algorithms for problems that are too complex or high-dimensional to allow exact solutions to be computed in reasonable time. This new paper offers an interesting set of theory and empirical results that point to an improvement in a popular existing approximate algorithm for Bayesian inference,” says Andrew Gelman ’85, ’86, a professor of statistics and political science at Columbia University, who was not involved with the study. “As one of the team involved in the creation of that earlier work, I’m happy to see our algorithm superseded by something more stable.”

    This research was supported by a National Science Foundation CAREER Award and the U.S. Office of Naval Research.  More