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    Creating new skills and new connections with MIT’s Quantitative Methods Workshop

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    Starting on New Year’s Day, when many people were still clinging to holiday revelry, scores of students and faculty members from about a dozen partner universities instead flipped open their laptops for MIT’s Quantitative Methods Workshop, a jam-packed, weeklong introduction to how computational and mathematical techniques can be applied to neuroscience and biology research. But don’t think of QMW as a “crash course.” Instead the program’s purpose is to help elevate each participant’s scientific outlook, both through the skills and concepts it imparts and the community it creates.

    “It broadens their horizons, it shows them significant applications they’ve never thought of, and introduces them to people whom as researchers they will come to know and perhaps collaborate with one day,” says Susan L. Epstein, a Hunter College computer science professor and education coordinator of MIT’s Center for Brains, Minds, and Machines, which hosts the program with the departments of Biology and Brain and Cognitive Sciences and The Picower Institute for Learning and Memory. “It is a model of interdisciplinary scholarship.”

    This year 83 undergraduates and faculty members from institutions that primarily serve groups underrepresented in STEM fields took part in the QMW, says organizer Mandana Sassanfar, senior lecturer and director of diversity and science outreach across the four hosting MIT entities. Since the workshop launched in 2010, it has engaged more than 1,000 participants, of whom more than 170 have gone on to participate in MIT Summer Research Programs (such as MSRP-BIO), and 39 have come to MIT for graduate school.

    Individual goals, shared experience

    Undergraduates and faculty in various STEM disciplines often come to QMW to gain an understanding of, or expand their expertise in, computational and mathematical data analysis. Computer science- and statistics-minded participants come to learn more about how such techniques can be applied in life sciences fields. In lectures; in hands-on labs where they used the computer programming language Python to process, analyze, and visualize data; and in less formal settings such as tours and lunches with MIT faculty, participants worked and learned together, and informed each other’s perspectives.

    Brain and Cognitive Sciences Professor Nancy Kanwisher delivers a lecture in MIT’s Building 46 on functional brain imaging to QMW participants.

    Photo: Mandana Sassanfar

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    And regardless of their field of study, participants made connections with each other and with the MIT students and faculty who taught and spoke over the course of the week.

    Hunter College computer science sophomore Vlad Vostrikov says that while he has already worked with machine learning and other programming concepts, he was interested to “branch out” by seeing how they are used to analyze scientific datasets. He also valued the chance to learn the experiences of the graduate students who teach QMW’s hands-on labs.

    “This was a good way to explore computational biology and neuroscience,” Vostrikov says. “I also really enjoy hearing from the people who teach us. It’s interesting to hear where they come from and what they are doing.”

    Jariatu Kargbo, a biology and chemistry sophomore at University of Maryland Baltimore County, says when she first learned of the QMW she wasn’t sure it was for her. It seemed very computation-focused. But her advisor Holly Willoughby encouraged Kargbo to attend to learn about how programming could be useful in future research — currently she is taking part in research on the retina at UMBC. More than that, Kargbo also realized it would be a good opportunity to make connections at MIT in advance of perhaps applying for MSRP this summer.

    “I thought this would be a great way to meet up with faculty and see what the environment is like here because I’ve never been to MIT before,” Kargbo says. “It’s always good to meet other people in your field and grow your network.”

    QMW is not just for students. It’s also for their professors, who said they can gain valuable professional education for their research and teaching.

    Fayuan Wen, an assistant professor of biology at Howard University, is no stranger to computational biology, having performed big data genetic analyses of sickle cell disease (SCD). But she’s mostly worked with the R programming language and QMW’s focus is on Python. As she looks ahead to projects in which she wants analyze genomic data to help predict disease outcomes in SCD and HIV, she says a QMW session delivered by biology graduate student Hannah Jacobs was perfectly on point.

    “This workshop has the skills I want to have,” Wen says.

    Moreover, Wen says she is looking to start a machine-learning class in the Howard biology department and was inspired by some of the teaching materials she encountered at QMW — for example, online curriculum modules developed by Taylor Baum, an MIT graduate student in electrical engineering and computer science and Picower Institute labs, and Paloma Sánchez-Jáuregui, a coordinator who works with Sassanfar.

    Tiziana Ligorio, a Hunter College computer science doctoral lecturer who together with Epstein teaches a deep machine-learning class at the City University of New York campus, felt similarly. Rather than require a bunch of prerequisites that might drive students away from the class, Ligorio was looking to QMW’s intense but introductory curriculum as a resource for designing a more inclusive way of getting students ready for the class.

    Instructive interactions

    Each day runs from 9 a.m. to 5 p.m., including morning and afternoon lectures and hands-on sessions. Class topics ranged from statistical data analysis and machine learning to brain-computer interfaces, brain imaging, signal processing of neural activity data, and cryogenic electron microscopy.

    “This workshop could not happen without dedicated instructors — grad students, postdocs, and faculty — who volunteer to give lectures, design and teach hands-on computer labs, and meet with students during the very first week of January,” Saassanfar says.

    MIT assistant professor of biology Brady Weissbourd (center) converses with QMW student participants during a lunch break.

    Photo: Mandana Sassanfar

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    The sessions surround student lunches with MIT faculty members. For example, at midday Jan. 2, assistant professor of biology Brady Weissbourd, an investigator in the Picower Institute, sat down with seven students in one of Building 46’s curved sofas to field questions about his neuroscience research in jellyfish and how he uses quantitative techniques as part of that work. He also described what it’s like to be a professor, and other topics that came to the students’ minds.

    Then the participants all crossed Vassar Street to Building 26’s Room 152, where they formed different but similarly sized groups for the hands-on lab “Machine learning applications to studying the brain,” taught by Baum. She guided the class through Python exercises she developed illustrating “supervised” and “unsupervised” forms of machine learning, including how the latter method can be used to discern what a person is seeing based on magnetic readings of brain activity.

    As students worked through the exercises, tablemates helped each other by supplementing Baum’s instruction. Ligorio, Vostrikov, and Kayla Blincow, assistant professor of biology at the University of the Virgin Islands, for instance, all leapt to their feet to help at their tables.

    Hunter College lecturer of computer science Tiziana Ligorio (standing) explains a Python programming concept to students at her table during a workshop session.

    Photo: David Orenstein

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    At the end of the class, when Baum asked students what they had learned, they offered a litany of new knowledge. Survey data that Sassanfar and Sánchez-Jáuregui use to anonymously track QMW outcomes, revealed many more such attestations of the value of the sessions. With a prompt asking how one might apply what they’ve learned, one respondent wrote: “Pursue a research career or endeavor in which I apply the concepts of computer science and neuroscience together.”

    Enduring connections

    While some new QMW attendees might only be able to speculate about how they’ll apply their new skills and relationships, Luis Miguel de Jesús Astacio could testify to how attending QMW as an undergraduate back in 2014 figured into a career where he is now a faculty member in physics at the University of Puerto Rico Rio Piedras Campus. After QMW, he returned to MIT that summer as a student in the lab of neuroscientist and Picower Professor Susumu Tonegawa. He came back again in 2016 to the lab of physicist and Francis Friedman Professor Mehran Kardar. What’s endured for the decade has been his connection to Sassanfar. So while he was once a student at QMW, this year he was back with a cohort of undergraduates as a faculty member.

    Michael Aldarondo-Jeffries, director of academic advancement programs at the University of Central Florida, seconded the value of the networking that takes place at QMW. He has brought students for a decade, including four this year. What he’s observed is that as students come together in settings like QMW or UCF’s McNair program, which helps to prepare students for graduate school, they become inspired about a potential future as researchers.

    “The thing that stands out is just the community that’s formed,” he says. “For many of the students, it’s the first time that they’re in a group that understands what they’re moving toward. They don’t have to explain why they’re excited to read papers on a Friday night.”

    Or why they are excited to spend a week including New Year’s Day at MIT learning how to apply quantitative methods to life sciences data. More

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      Inclusive research for social change

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      Pair a decades-old program dedicated to creating research opportunities for underrepresented minorities and populations with a growing initiative committed to tackling the very issues at the heart of such disparities, and you’ll get a transformative partnership that only MIT can deliver. 

      Since 1986, the MIT Summer Research Program (MSRP) has led an institutional effort to prepare underrepresented students (minorities, women in STEM, or students with low socioeconomic status) for doctoral education by pairing them with MIT labs and research groups. For the past three years, the Initiative on Combatting Systemic Racism (ICSR), a cross-disciplinary research collaboration led by MIT’s Institute for Data, Systems, and Society (IDSS), has joined them in their mission, helping bring the issue full circle by providing MSRP students with the opportunity to use big data and computational tools to create impactful changes toward racial equity.

      “ICSR has further enabled our direct engagement with undergrads, both within and outside of MIT,” says Fotini Christia, the Ford International Professor of the Social Sciences, associate director of IDSS, and co-organizer for the initiative. “We’ve found that this line of research has attracted students interested in examining these topics with the most rigorous methods.”

      The initiative fits well under the IDSS banner, as IDSS research seeks solutions to complex societal issues through a multidisciplinary approach that includes statistics, computation, modeling, social science methodologies, human behavior, and an understanding of complex systems. With the support of faculty and researchers from all five schools and the MIT Schwarzman College of Computing, the objective of ICSR is to work on an array of different societal aspects of systemic racism through a set of verticals including policing, housing, health care, and social media.

      Where passion meets impact

      Grinnell senior Mia Hines has always dreamed of using her love for computer science to support social justice. She has experience working with unhoused people and labor unions, and advocating for Indigenous peoples’ rights. When applying to college, she focused her essay on using technology to help Syrian refugees.

      “As a Black woman, it’s very important to me that we focus on these areas, especially on how we can use technology to help marginalized communities,” Hines says. “And also, how do we stop technology or improve technology that is already hurting marginalized communities?”   

      Through MSRP, Hines was paired with research advisor Ufuoma Ovienmhada, a fourth-year doctoral student in the Department of Aeronautics and Astronautics at MIT. A member of Professor Danielle Wood’s Space Enabled research group at MIT’s Media Lab, Ovienmhada received funding from an ICSR Seed Grant and NASA’s Applied Sciences Program to support her ongoing research measuring environmental injustice and socioeconomic disparities in prison landscapes. 

      “I had been doing satellite remote sensing for environmental challenges and sustainability, starting out looking at coastal ecosystems, when I learned about an issue called ‘prison ecology,’” Ovienmhada explains. “This refers to the intersection of mass incarceration and environmental justice.”

      Ovienmhada’s research uses satellite remote sensing and environmental data to characterize exposures to different environmental hazards such as air pollution, extreme heat, and flooding. “This allows others to use these datasets for real-time advocacy, in addition to creating public awareness,” she says.

      Focused especially on extreme heat, Hines used satellite remote sensing to monitor the fluctuation of temperature to assess the risk being imposed on prisoners, including death, especially in states like Texas, where 75 percent of prisons either don’t have full air conditioning or have none at all.

      “Before this project I had done little to no work with geospatial data, and as a budding data scientist, getting to work with and understanding different types of data and resources is really helpful,” Hines says. “I was also funded and afforded the flexibility to take advantage of IDSS’s Data Science and Machine Learning online course. It was really great to be able to do that and learn even more.”

      Filling the gap

      Much like Hines, Harvey Mudd senior Megan Li was specifically interested in the IDSS-supported MSRP projects. She was drawn to the interdisciplinary approach, and she seeks in her own work to apply computational methods to societal issues and to make computer science more inclusive, considerate, and ethical. 

      Working with Aurora Zhang, a grad student in IDSS’s Social and Engineering Systems PhD program, Li used county-level data on income and housing prices to quantify and visualize how affordability based on income alone varies across the United States. She then expanded the analysis to include assets and debt to determine the most common barriers to home ownership.

      “I spent my day-to-day looking at census data and writing Python scripts that could work with it,” reports Li. “I also reached out to the Census Bureau directly to learn a little bit more about how they did their data collection, and discussed questions related to some of their previous studies and working papers that I had reviewed.” 

      Outside of actual day-to-day research, Li says she learned a lot in conversations with fellow researchers, particularly changing her “skeptical view” of whether or not mortgage lending algorithms would help or hurt home buyers in the approval process. “I think I have a little bit more faith now, which is a good thing.”

      “Harvey Mudd is undergraduate-only, and while professors do run labs here, my specific research areas are not well represented,” Li says. “This opportunity was enormous in that I got the experience I need to see if this research area is actually something that I want to do long term, and I got more mirrors into what I would be doing in grad school from talking to students and getting to know faculty.”

      Closing the loop

      While participating in MSRP offered crucial research experience to Hines, the ICSR projects enabled her to engage in topics she’s passionate about and work that could drive tangible societal change.

      “The experience felt much more concrete because we were working on these very sophisticated projects, in a supportive environment where people were very excited to work with us,” she says.

      A significant benefit for Li was the chance to steer her research in alignment with her own interests. “I was actually given the opportunity to propose my own research idea, versus supporting a graduate student’s work in progress,” she explains. 

      For Ovienmhada, the pairing of the two initiatives solidifies the efforts of MSRP and closes a crucial loop in diversity, equity, and inclusion advocacy. 

      “I’ve participated in a lot of different DEI-related efforts and advocacy and one thing that always comes up is the fact that it’s not just about bringing people in, it’s also about creating an environment and opportunities that align with people’s values,” Ovienmhada says. “Programs like MSRP and ICSR create opportunities for people who want to do work that’s aligned with certain values by providing the needed mentoring and financial support.” More

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      Making genetic prediction models more inclusive

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      While any two human genomes are about 99.9 percent identical, genetic variation in the remaining 0.1 percent plays an important role in shaping human diversity, including a person’s risk for developing certain diseases.

      Measuring the cumulative effect of these small genetic differences can provide an estimate of an individual’s genetic risk for a particular disease or their likelihood of having a particular trait. However, the majority of models used to generate these “polygenic scores” are based on studies done in people of European descent, and do not accurately gauge the risk for people of non-European ancestry or people whose genomes contain a mixture of chromosome regions inherited from previously isolated populations, also known as admixed ancestry.

      In an effort to make these genetic scores more inclusive, MIT researchers have created a new model that takes into account genetic information from people from a wider diversity of genetic ancestries across the world. Using this model, they showed that they could increase the accuracy of genetics-based predictions for a variety of traits, especially for people from populations that have been traditionally underrepresented in genetic studies.

      “For people of African ancestry, our model proved to be about 60 percent more accurate on average,” says Manolis Kellis, a professor of computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Broad Institute of MIT and Harvard. “For people of admixed genetic backgrounds more broadly, who have been excluded from most previous models, the accuracy of our model increased by an average of about 18 percent.”

      The researchers hope their more inclusive modeling approach could help improve health outcomes for a wider range of people and promote health equity by spreading the benefits of genomic sequencing more widely across the globe.

      “What we have done is created a method that allows you to be much more accurate for admixed and ancestry-diverse individuals, and ensure the results and the benefits of human genetics research are equally shared by everyone,” says MIT postdoc Yosuke Tanigawa, the lead and co-corresponding author of the paper, which appears today in open-access form in the American Journal of Human Genetics. The researchers have made all of their data publicly available for the broader scientific community to use.

      More inclusive models

      The work builds on the Human Genome Project, which mapped all of the genes found in the human genome, and on subsequent large-scale, cohort-based studies of how genetic variants in the human genome are linked to disease risk and other differences between individuals.

      These studies showed that the effect of any individual genetic variant on its own is typically very small. Together, these small effects add up and influence the risk of developing heart disease or diabetes, having a stroke, or being diagnosed with psychiatric disorders such as schizophrenia.

      “We have hundreds of thousands of genetic variants that are associated with complex traits, each of which is individually playing a weak effect, but together they are beginning to be predictive for disease predispositions,” Kellis says.

      However, most of these genome-wide association studies included few people of non-European descent, so polygenic risk models based on them translate poorly to non-European populations. People from different geographic areas can have different patterns of genetic variation, shaped by stochastic drift, population history, and environmental factors — for example, in people of African descent, genetic variants that protect against malaria are more common than in other populations. Those variants also affect other traits involving the immune system, such as counts of neutrophils, a type of immune cell. That variation would not be well-captured in a model based on genetic analysis of people of European ancestry alone.

      “If you are an individual of African descent, of Latin American descent, of Asian descent, then you are currently being left out by the system,” Kellis says. “This inequity in the utilization of genetic information for predicting risk of patients can cause unnecessary burden, unnecessary deaths, and unnecessary lack of prevention, and that’s where our work comes in.”

      Some researchers have begun trying to address these disparities by creating distinct models for people of European descent, of African descent, or of Asian descent. These emerging approaches assign individuals to distinct genetic ancestry groups, aggregate the data to create an association summary, and make genetic prediction models. However, these approaches still don’t represent people of admixed genetic backgrounds well.

      “Our approach builds on the previous work without requiring researchers to assign individuals or local genomic segments of individuals to predefined distinct genetic ancestry groups,” Tanigawa says. “Instead, we develop a single model for everybody by directly working on individuals across the continuum of their genetic ancestries.”

      In creating their new model, the MIT team used computational and statistical techniques that enabled them to study each individual’s unique genetic profile instead of grouping individuals by population. This methodological advancement allowed the researchers to include people of admixed ancestry, who made up nearly 10 percent of the UK Biobank dataset used for this study and currently account for about one in seven newborns in the United States.

      “Because we work at the individual level, there is no need for computing summary-level data for different populations,” Kellis says. “Thus, we did not need to exclude individuals of admixed ancestry, increasing our power by including more individuals and representing contributions from all populations in our combined model.”

      Better predictions

      To create their new model, the researchers used genetic data from more than 280,000 people, which was collected by UK Biobank, a large-scale biomedical database and research resource containing de-identified genetic, lifestyle, and health information from half a million U.K. participants. Using another set of about 81,000 held-out individuals from the UK Biobank, the researchers evaluated their model across 60 traits, which included traits related to body size and shape, such as height and body mass index, as well as blood traits such as white blood cell count and red blood cell count, which also have a genetic basis.

      The researchers found that, compared to models trained only on European-ancestry individuals, their model’s predictions are more accurate for all genetic ancestry groups. The most notable gain was for people of African ancestry, who showed 61 percent average improvements, even though they only made up about 1.5 percent of samples in UK Biobank. The researchers also saw improvements of 11 percent for people of South Asian descent and 5 percent for white British people. Predictions for people of admixed ancestry improved by about 18 percent.

      “When you bring all the individuals together in the training set, everybody contributes to the training of the polygenic score modeling on equal footing,” Tanigawa says. “Combined with increasingly more inclusive data collection efforts, our method can help leverage these efforts to improve predictive accuracy for all.”

      The MIT team hopes its approach can eventually be incorporated into tests of an individual’s risk of a variety of diseases. Such tests could be combined with conventional risk factors and used to help doctors diagnose disease or to help people manage their risk for certain diseases before they develop.

      “Our work highlights the power of diversity, equity, and inclusion efforts in the context of genomics research,” Tanigawa says.

      The researchers now hope to add even more data to their model, including data from the United States, and to apply it to additional traits that they didn’t analyze in this study.

      “This is just the start,” Kellis says. “We can’t wait to see more people join our effort to propel inclusive human genetics research.”

      The research was funded by the National Institutes of Health. More

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      MIT welcomes nine MLK Visiting Professors and Scholars for 2023-24

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      Established in 1990, the MLK Visiting Professors and Scholars Program at MIT welcomes outstanding scholars to the Institute for visiting appointments. MIT aspires to attract candidates who are, in the words of Martin Luther King Jr., “trailblazers in human, academic, scientific and religious freedom.” The program honors King’s life and legacy by expanding and extending the reach of our community. 

      The MLK Scholars Program has welcomed more than 140 professors, practitioners, and professionals at the forefront of their respective fields to MIT. They contribute to the growth and enrichment of the community through their interactions with students, staff, and faculty. They pay tribute to Martin Luther King Jr.’s life and legacy of service and social justice, and they embody MIT’s values: excellence and curiosity, openness and respect, and belonging and community.  

      Each new cohort of scholars actively participates in community engagement and supports MIT’s mission of “advancing knowledge and educating students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century.” 

      The 2023-2024 MLK Scholars:

      Tawanna Dillahunt is an associate professor at the University of Michigan’s School of Information with a joint appointment in their electrical engineering and computer science department. She is joining MIT at the end of a one-year visiting appointment as a Harvard Radcliffe Fellow. Her faculty hosts at the Institute are Catherine D’Ignazio in the Department of Urban Studies and Planning and Fotini Christia in the Institute for Data, Systems, and Society (IDSS). Dillahunt’s research focuses on equitable and inclusive computing. During her appointment, she will host a podcast to explore ethical and socially responsible ways to engage with communities, with a special emphasis on technology. 

      Kwabena Donkor is an assistant professor of marketing at Stanford Graduate School of Business; he is hosted by Dean Eckles, an associate professor of marketing at MIT Sloan School of Management. Donkor’s work bridges economics, psychology, and marketing. His scholarship combines insights from behavioral economics with data and field experiments to study social norms, identity, and how these constructs interact with policy in the marketplace.

      Denise Frazier joins MIT from Tulane University, where she is an assistant director in the New Orleans Center for the Gulf South. She is a researcher and performer and brings a unique interdisciplinary approach to her work at the intersection of cultural studies, environmental justice, and music. Frazier is hosted by Christine Ortiz, the Morris Cohen Professor in the Department of Materials Science and Engineering. 

      Wasalu Jaco, an accomplished performer and artist, is renewing his appointment at MIT for a second year; he is hosted jointly by Nick Montfort, a professor of digital media in the Comparative Media Studies Program/Writing, and Mary Fuller, a professor in the Literature Section and the current chair of the MIT faculty. In his second year, Jaco will work on Cyber/Cypher Rapper, a research project to develop a computational system that participates in responsive and improvisational rap.

      Morgane Konig first joined the Center for Theoretical Physics at MIT in December 2021 as a postdoc. Now a member of the 2023–24 MLK Visiting Scholars Program cohort, she will deepen her ties with scholars and research groups working in cosmology, primarily on early-universe inflation and late-universe signatures that could enable the scientific community to learn more about the mysterious nature of dark matter and dark energy. Her faculty hosts are David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, and Alan Guth, the Victor F. Weisskopf Professor of Physics, both from the Department of Physics.

      The former minister of culture for Colombia and a transformational leader dedicated to environmental protection, Angelica Mayolo-Obregon joins MIT from Buenaventura, Colombia. During her time at MIT, she will serve as an advisor and guest speaker, and help MIT facilitate gatherings of environmental leaders committed to addressing climate action and conserving biodiversity across the Americas, with a special emphasis on Afro-descendant communities. Mayolo-Obregon is hosted by John Fernandez, a professor of building technology in the Department of Architecture and director of MIT’s Environmental Solutions Initiative, and by J. Phillip Thompson, an associate professor in the Department of Urban Studies and Planning (and a former MLK Scholar).

      Jean-Luc Pierite is a member of the Tunica-Biloxi Tribe of Louisiana and the president of the board of directors of North American Indian Center of Boston. While at MIT, Pierite will build connections between MIT and the local Indigenous communities. His research focuses on enhancing climate resilience planning by infusing Indigenous knowledge and ecological practices into scientific and other disciplines. His faculty host is Janelle Knox-Hayes, the Lister Brothers Professor of Economic Geography and Planning in the Department of Urban Studies and Planning.

      Christine Taylor-Butler ’81 is a children’s book author who has written over 90 books; she is hosted by Graham Jones, an associate professor of anthropology. An advocate for literacy and STEAM education in underserved urban and rural schools, Taylor-Butler will partner with community organizations in the Boston area. She is also completing the fourth installment of her middle-grade series, “The Lost Tribe.” These books follow a team of five kids as they use science and technology to crack codes and solve mysteries.

      Angelino Viceisza, a professor of economics at Spelman College, joins MIT Sloan as an MLK Visiting Professor and the Phyllis Wallace Visiting Professor; he is hosted by Robert Gibbons, Sloan Distinguished Professor of Management, and Ray Reagans, Alfred P. Sloan Professor of Management, professor of organization studies, and associate dean for diversity, equity, and inclusion at MIT Sloan. Viceisza has strong, ongoing connections with MIT. His research focuses on remittances, retirement, and household finance in low-income countries and is relevant to public finance and financial economics, as well as the development and organizational economics communities at MIT. 

      Javit Drake, Moriba Jah, and Louis Massiah, members of last year’s cohort of MLK Scholars, will remain at MIT through the end of 2023.

      There are multiple opportunities throughout the year to meet our MLK Visiting Scholars and learn more about their research projects and their social impact. 

      For more information about the MLK Visiting Professors and Scholars Program and upcoming events, visit the website. More

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      On the hunt for sustainable materials

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      By the time she started high school, Avni Singhal had attended six different schools in a variety of settings, from a traditional public school to a self-paced program. The transitions opened her eyes to how widely educational environments can vary, and made her think about that impact on students.

      “Experiencing so many different types of educational systems exposed me to different ways of looking at things and how that shapes people’s worldviews,” says Singhal.

      Now a fourth-year PhD student in the Department of Materials Science and Engineering, Singhal is still thinking about increasing opportunities for her fellow students, while also pursuing her research. She devotes herself to both developing sustainable materials and improving the graduate experience in her department.

      She recently completed her two-year term as a student representative on the department’s graduate studies committee. In this role, she helped revamp the communication around the qualifying exams and introducing student input to the faculty search process.

      “It’s given me a lot of insight into how our department works,” says Singhal. “It’s a chance to get to know faculty, bring up issues that students experience, and work on changing things that we think could be improved.”

      At the same time, Singhal uses atomistic simulations to model material properties, with an eye toward sustainability. She is a part of the Learning Matter Lab, a group that merges data science tools with engineering and physics-based simulation to better design and understand materials. As part of a computational group, Singhal has worked on a range of projects in collaboration with other labs that are looking to combine computing with other disciplines. Some of this work is sponsored by the MIT Climate and Sustainability Consortium, which facilitates connections across MIT labs and industry.

      Joining the Learning Matter Lab was a step out of Singhal’s comfort zone. She arrived at MIT from the University of California at Berkeley with a joint degree in materials science and bioengineering, as well as a degree in electrical engineering and computer science.

      “I was generally interested in doing work on environment-related applications,” says Singhal. “I was pretty hesitant at first to switch entirely to computation because it’s a very different type of lifestyle of research than what I was doing before.”

      Singhal has taken the challenge in stride, contributing to projects including improving carbon capture molecules and developing new deconstructable, degradable plastics. Not only does Singhal have to understand the technical details of her own work, she also needs to understand the big picture and how to best wield the expertise of her collaborators.

      “When I came in, I was very wide-eyed, thinking computation can do everything because I had never done it before,” says Singhal. “It’s that curve where you know a little bit about something, and you think it can do everything. And then as you learn more, you learn where it can and can’t help us, where it can be valuable, and how to figure out in what part of a project it’s useful.”

      Singhal applies a similarly critical lens when thinking about graduate school as a whole. She notes that access to information and resources is often the main factor determining who enters selective educational programs, and that such access becomes increasingly limited at the graduate level.

      “I realized just how much applying is a function of knowing how to do it,” says Singhal, who co-organized and volunteers with the DMSE Application Assistance Program. The program matches prospective applicants with current students to give feedback on their application materials and provide insight into what it’s like attending MIT. Some of the first students Singhal mentored through the program are now also participants as well.

      “The further you get in your educational career, the more you realize how much assistance you got along the way to get where you are,” says Singhal. “That happens at every stage.”

      Looking toward the future, Singhal wants to continue to pursue research with a sustainability impact. She also wants to continue mentoring in some capacity but isn’t in a rush to figure out exactly what that will look like.

      “Grad school doesn’t mean I have to do one thing. I can stay open to all the possibilities of what comes next.”  More

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      Fast-tracking fusion energy’s arrival with AI and accessibility

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      As the impacts of climate change continue to grow, so does interest in fusion’s potential as a clean energy source. While fusion reactions have been studied in laboratories since the 1930s, there are still many critical questions scientists must answer to make fusion power a reality, and time is of the essence. As part of their strategy to accelerate fusion energy’s arrival and reach carbon neutrality by 2050, the U.S. Department of Energy (DoE) has announced new funding for a project led by researchers at MIT’s Plasma Science and Fusion Center (PSFC) and four collaborating institutions.

      Cristina Rea, a research scientist and group leader at the PSFC, will serve as the primary investigator for the newly funded three-year collaboration to pilot the integration of fusion data into a system that can be read by AI-powered tools. The PSFC, together with scientists from William & Mary, the University of Wisconsin at Madison, Auburn University, and the nonprofit HDF Group, plan to create a holistic fusion data platform, the elements of which could offer unprecedented access for researchers, especially underrepresented students. The project aims to encourage diverse participation in fusion and data science, both in academia and the workforce, through outreach programs led by the group’s co-investigators, of whom four out of five are women. 

      The DoE’s award, part of a $29 million funding package for seven projects across 19 institutions, will support the group’s efforts to distribute data produced by fusion devices like the PSFC’s Alcator C-Mod, a donut-shaped “tokamak” that utilized powerful magnets to control and confine fusion reactions. Alcator C-Mod operated from 1991 to 2016 and its data are still being studied, thanks in part to the PSFC’s commitment to the free exchange of knowledge.

      Currently, there are nearly 50 public experimental magnetic confinement-type fusion devices; however, both historical and current data from these devices can be difficult to access. Some fusion databases require signing user agreements, and not all data are catalogued and organized the same way. Moreover, it can be difficult to leverage machine learning, a class of AI tools, for data analysis and to enable scientific discovery without time-consuming data reorganization. The result is fewer scientists working on fusion, greater barriers to discovery, and a bottleneck in harnessing AI to accelerate progress.

      The project’s proposed data platform addresses technical barriers by being FAIR — Findable, Interoperable, Accessible, Reusable — and by adhering to UNESCO’s Open Science (OS) recommendations to improve the transparency and inclusivity of science; all of the researchers’ deliverables will adhere to FAIR and OS principles, as required by the DoE. The platform’s databases will be built using MDSplusML, an upgraded version of the MDSplus open-source software developed by PSFC researchers in the 1980s to catalogue the results of Alcator C-Mod’s experiments. Today, nearly 40 fusion research institutes use MDSplus to store and provide external access to their fusion data. The release of MDSplusML aims to continue that legacy of open collaboration.

      The researchers intend to address barriers to participation for women and disadvantaged groups not only by improving general access to fusion data, but also through a subsidized summer school that will focus on topics at the intersection of fusion and machine learning, which will be held at William & Mary for the next three years.

      Of the importance of their research, Rea says, “This project is about responding to the fusion community’s needs and setting ourselves up for success. Scientific advancements in fusion are enabled via multidisciplinary collaboration and cross-pollination, so accessibility is absolutely essential. I think we all understand now that diverse communities have more diverse ideas, and they allow faster problem-solving.”

      The collaboration’s work also aligns with vital areas of research identified in the International Atomic Energy Agency’s “AI for Fusion” Coordinated Research Project (CRP). Rea was selected as the technical coordinator for the IAEA’s CRP emphasizing community engagement and knowledge access to accelerate fusion research and development. In a letter of support written for the group’s proposed project, the IAEA stated that, “the work [the researchers] will carry out […] will be beneficial not only to our CRP but also to the international fusion community in large.”

      PSFC Director and Hitachi America Professor of Engineering Dennis Whyte adds, “I am thrilled to see PSFC and our collaborators be at the forefront of applying new AI tools while simultaneously encouraging and enabling extraction of critical data from our experiments.”

      “Having the opportunity to lead such an important project is extremely meaningful, and I feel a responsibility to show that women are leaders in STEM,” says Rea. “We have an incredible team, strongly motivated to improve our fusion ecosystem and to contribute to making fusion energy a reality.” More

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      Summer research offers a springboard to advanced studies

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      Doctoral studies at MIT aren’t a calling for everyone, but they can be for anyone who has had opportunities to discover that science and technology research is their passion and to build the experience and skills to succeed. For Taylor Baum, Josefina Correa Menéndez, and Karla Alejandra Montejo, three graduate students in just one lab of The Picower Institute for Learning and Memory, a pivotal opportunity came via the MIT Summer Research Program in Biology and Neuroscience (MSRP-Bio). When a student finds MSRP-Bio, it helps them find their future in research. 

      In the program, undergraduate STEM majors from outside MIT spend the summer doing full-time research in the departments of Biology, Brain and Cognitive Sciences (BCS), or the Center for Brains, Minds and Machines (CBMM). They gain lab skills, mentoring, preparation for graduate school, and connections that might last a lifetime. Over the last two decades, a total of 215 students from underrepresented minority groups, who are from economically disadvantaged backgrounds, first-generation or nontraditional college students, or students with disabilities have participated in research in BCS or CBMM labs.  

      Like Baum, Correa Menéndez, and Montejo, the vast majority go on to pursue graduate studies, says Diversity and Outreach Coordinator Mandana Sassanfar, who runs the program. For instance, among 91 students who have worked in Picower Institute labs, 81 have completed their undergraduate studies. Of those, 46 enrolled in PhD programs at MIT or other schools such as Cornell, Yale, Stanford, and Princeton universities, and the University of California System. Another 12 have gone to medical school, another seven are in MD/PhD programs, and three have earned master’s degrees. The rest are studying as post-baccalaureates or went straight into the workforce after earning their bachelor’s degree. 

      After participating in the program, Baum, Correa Menéndez, and Montejo each became graduate students in the research group of Emery N. Brown, the Edward Hood Taplin Professor of Computational Neuroscience and Medical Engineering in The Picower Institute and the Institute for Medical Engineering and Science. The lab combines statistical, computational, and experimental neuroscience methods to study how general anesthesia affects the central nervous system to ultimately improve patient care and advance understanding of the brain. Brown says the students have each been doing “off-the-scale” work, in keeping with the excellence he’s seen from MSRP BIO students over the years. For example, on Aug. 10 Baum and Correa Menéndez were honored with MathWorks Fellowships.

      “I think MSRP is fantastic. Mandana does this amazing job of getting students who are quite talented to come to MIT to realize that they can move their game to the next level. They have the capacity to do it. They just need the opportunities,” Brown says. “These students live up to the expectations that you have of them. And now as graduate students, they’re taking on hard problems and they’re solving them.” 

      Paths to PhD studies 

      Pursuing a PhD is hardly a given. Many young students have never considered graduate school or specific fields of study like neuroscience or electrical engineering. But Sassanfar engages students across the country to introduce them to the opportunity MSRP-Bio provides to gain exposure, experience, and mentoring in advanced fields. Every fall, after the program’s students have returned to their undergraduate institutions, she visits schools in places as far flung as Florida, Maryland, Puerto Rico, and Texas and goes to conferences for diverse science communities such as ABRCMS and SACNAS to spread the word. 

      Taylor Baum

      Photo courtesy of Taylor Baum.

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      When Baum first connected with the program in 2017, she was finding her way at Penn State University. She had been majoring in biology and music composition but had just switched the latter to engineering following a conversation over coffee exposing her to brain-computer interfacing technology, in which detecting brain signals of people with full-body paralysis could improve their quality of life by enabling control of computers or wheelchairs. Baum became enthusiastic about the potential to build similar systems, but as a new engineering student, she struggled to find summer internships and research opportunities. 

      “I got rejected from every single progam except the MIT Center for Brains, Minds and Machines MSRP,” she recalls with a chuckle. 

      Baum thrived in MSRP-Bio, working in Brown’s lab for three successive summers. At each stage, she said, she gained more research skills, experience, and independence. When she graduated, she was sure she wanted to go to graduate school and applied to four of her dream schools. She accepted MIT’s offer to join the Department of Electrical Engineering and Computer Science, where she is co-advised by faculty members there and by Brown. She is now working to develop a system grounded in cardiovascular physiology that can improve blood pressure management. A tool for practicing anesthesiologists, the system automates the dosing of drugs to maintain a patient’s blood pressure at safe levels in the operating room or intensive care unit. 

      More than that, Baum not only is leading an organization advancing STEM education in Puerto Rico, but also is helping to mentor a current MSRP-Bio student in the Brown lab. 

      “MSRP definitely bonds everyone who has participated in it,” Baum says. “If I see anyone who I know participated in MSRP, we could have an immediate conversation. I know that most of us, if we needed help, we’d feel comfortable asking for help from someone from MSRP. With that shared experience, we have a sense of camaraderie, and community.” 

      In fact, a few years ago when a former MSRP-Bio student named Karla Montejo was applying to MIT, Baum provided essential advice and feedback about the application process, Montejo says. Now, as a graduate student, Montejo has become a mentor for the program in her own right, Sassanfar notes. For instance, Montejo serves on program alumni panels that advise new MSRP-Bio students. 

      Karla Alejandra Montejo

      Photo courtesy of Karla Alejandra Montejo.

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      Montejo’s family immigrated to Miami from Cuba when she was a child. The magnet high school she attended was so new that students were encouraged to help establish the school’s programs. She forged a path into research. 

      “I didn’t even know what research was,” she says. “I wanted to be a doctor, and I thought maybe it would help me on my resume. I thought it would be kind of like shadowing, but no, it was really different. So I got really captured by research when I was in high school.” 

      Despite continuing to pursue research in college at Florida International University, Montejo didn’t get into graduate school on her first attempt because she hadn’t yet learned how to focus her application. But Sassanfar had visited FIU to recruit students and through that relationship Montejo had already gone through MIT’s related Quantitative Methods Workshop (QMW). So Montejo enrolled in MSRP-Bio, working in the CBMM-affiliated lab of Gabriel Kreiman at Boston Children’s Hospital. 

      “I feel like Mandana really helped me out, gave me a break, and the MSRP experience pretty much solidified that I really wanted to come to MIT,” Montejo says. 

      In the QMW, Montejo learned she really liked computational neuroscience, and in Kreiman’s lab she got to try her hand at computational modeling of the cognition involved in making perceptual sense of complex scenes. Montejo realized she wanted to work on more biologically based neuroscience problems. When the summer ended, because she was off the normal graduate school cycle for now, she found a two-year post-baccalaurate program at Mayo Clinic studying the role a brain cell type called astrocytes might have in the Parkinson’s disease treatment deep brain stimulation. 

      When it came time to reapply to graduate schools (with the help of Baum and others in the BCS Application Assistance Program) Montejo applied to MIT and got in, joining the Brown lab. Now she’s working on modeling the role of  metabolic processes in the changing of brain rhythms under anesthesia, taking advantage of how general anesthesia predictably changes brain states. The effects anesthetic drugs have on cell metabolism and the way that ultimately affects levels of consciousness reveals important aspects of how metabolism affects brain circuits and systems. Earlier this month, for instance, Montejo co-led a paper the lab published in The Proceedings of the National Academy of Sciences detailing the neuroscience of a patient’s transition into an especially deep state of unconsciousness called “burst suppression.” 

      Josefina Correa Menendez

      Photo: David Orenstein

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      A signature of the Brown lab’s work is rigorous statistical analysis and methods, for instance to discern brain arousal states from EEG measures of brain rhythms. A PhD candidate in MIT’s Interdisciplinary Doctoral Program in Statistics, Correa Menéndez is advancing the use of Bayesian hierarchical models for neural data analysis. These statistical models offer a principled way of pooling information across datasets. One of her models can help scientists better understand the way neurons can “spike” with electrical activity when the brain is presented with a stimulus. The other’s power is in discerning critical features such as arousal states of the brain under general anesthesia from electrophysiological recordings. 

      Though she now works with complex equations and computations as a PhD candidate in neuroscience and statistics, Correa Menéndez was mostly interested in music art as a high school student at Academia María Reina in San Juan and then architecture in college at the University of Puerto Rico at Río Piedras. It was discussions at the intersection of epistemology and art during an art theory class that inspired Correa Menéndez to switch her major to biology and to take computer science classes, too. 

      When Sassanfar visited Puerto Rico in 2017, a computer science professor (Patricia Ordóñez) suggested that Correa Menéndez apply for a chance to attend the QMW. She did, and that led her to also participate in MSRP-Bio in the lab of Sherman Fairchild Professor Matt Wilson (a faculty member in BCS, CBMM, and the Picower Institute). She joined in the lab’s studies of how spatial memories are represented in the hippocampus and how the brain makes use of those memories to help understand the world around it. With mentoring from then-postdoc Carmen Varela (now a faculty member at Florida State University), the experience not only exposed her to neuroscience, but also helped her gain skills and experience with lab experiments, building research tools, and conducting statistical analyses. She ended up working in the Wilson lab as a research scholar for a year and began her graduate studies in September 2018.  

      Classes she took with Brown as a research scholar inspired her to join his lab as a graduate student. 

      “Taking the classes with Emery and also doing experiments made me aware of the role of statistics in the scientific process: from the interpretation of results to the analysis and the design of experiments,” she says. “More often than not, in science, statistics becomes this sort of afterthought — this ‘annoying’ thing that people need to do to get their paper published. But statistics as a field is actually a lot more than that. It’s a way of thinking about data. Particularly, Bayesian modeling provides a principled inference framework for combining prior knowledge into a hypothesis that you can test with data.” 

      To be sure, no one starts out with such inspiration about scientific scholarship, but MSRP-Bio helps students find that passion for research and the paths that opens up.   More

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      in Data Management & Statistics

      Understanding viral justice

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      In the wake of the Covid-19 pandemic, the word “viral” has a new resonance, and it’s not necessarily positive. Ruha Benjamin, a scholar who investigates the social dimensions of science, medicine, and technology, advocates a shift in perspective. She thinks justice can also be contagious. That’s the premise of Benjamin’s award-winning book “Viral Justice: How We Grow the World We Want,” as she shared with MIT Libraries staff on a June 14 visit. 

      “If this pandemic has taught us anything, it’s that something almost undetectable can be deadly, and that we can transmit it without even knowing,” said Benjamin, professor of African American studies at Princeton University. “Doesn’t this imply that small things, seemingly minor actions, decisions, or habits, could have exponential effects in the other direction, tipping the scales towards justice?” 

      To seek a more just world, Benjamin exhorted library staff to notice the ways exclusion is built into our daily lives, showing examples of park benches with armrests at regular intervals. On the surface they appear welcoming, but they also make lying down — or sleeping — impossible. This idea is taken to the extreme with “Pay and Sit,” an art installation by Fabian Brunsing in the form of a bench that deploys sharp spikes on the seat if the user doesn’t pay a meter. It serves as a powerful metaphor for discriminatory design. 

      “Dr. Benjamin’s keynote was seriously mind-blowing,” said Cherry Ibrahim, human resources generalist in the MIT Libraries. “One part that really grabbed my attention was when she talked about benches purposely designed to prevent unhoused people from sleeping on them. There are these hidden spikes in our community that we might not even realize because they don’t directly impact us.” 

      Benjamin urged the audience to look for those “spikes,” which new technologies can make even more insidious — gender and racial bias in facial recognition, the use of racial data in software used to predict student success, algorithmic bias in health care — often in the guise of progress. She coined the term “the New Jim Code” to describe the combination of coded bias and the imagined objectivity we ascribe to technology. 

      “At the MIT Libraries, we’re deeply concerned with combating inequities through our work, whether it’s democratizing access to data or investigating ways disparate communities can participate in scholarship with minimal bias or barriers,” says Director of Libraries Chris Bourg. “It’s our mission to remove the ‘spikes’ in the systems through which we create, use, and share knowledge.”

      Calling out the harms encoded into our digital world is critical, argues Benjamin, but we must also create alternatives. This is where the collective power of individuals can be transformative. Benjamin shared examples of those who are “re-imagining the default settings of technology and society,” citing initiatives like Data for Black Lives movement and the Detroit Community Technology Project. “I’m interested in the way that everyday people are changing the digital ecosystem and demanding different kinds of rights and responsibilities and protections,” she said.

      In 2020, Benjamin founded the Ida B. Wells Just Data Lab with a goal of bringing together students, educators, activists, and artists to develop a critical and creative approach to data conception, production, and circulation. Its projects have examined different aspects of data and racial inequality: assessing the impact of Covid-19 on student learning; providing resources that confront the experience of Black mourning, grief, and mental health; or developing a playbook for Black maternal mental health. Through the lab’s student-led projects Benjamin sees the next generation re-imagining technology in ways that respond to the needs of marginalized people.

      “If inequity is woven into the very fabric of our society — we see it from policing to education to health care to work — then each twist, coil, and code is a chance for us to weave new patterns, practices, and politics,” she said. “The vastness of the problems that we’re up against will be their undoing.” More

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      Gaining real-world industry experience through Break Through Tech AI at MIT

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      Taking what they learned conceptually about artificial intelligence and machine learning (ML) this year, students from across the Greater Boston area had the opportunity to apply their new skills to real-world industry projects as part of an experiential learning opportunity offered through Break Through Tech AI at MIT.

      Hosted by the MIT Schwarzman College of Computing, Break Through Tech AI is a pilot program that aims to bridge the talent gap for women and underrepresented genders in computing fields by providing skills-based training, industry-relevant portfolios, and mentoring to undergraduate students in regional metropolitan areas in order to position them more competitively for careers in data science, machine learning, and artificial intelligence.

      “Programs like Break Through Tech AI gives us opportunities to connect with other students and other institutions, and allows us to bring MIT’s values of diversity, equity, and inclusion to the learning and application in the spaces that we hold,” says Alana Anderson, assistant dean of diversity, equity, and inclusion for the MIT Schwarzman College of Computing.

      The inaugural cohort of 33 undergraduates from 18 Greater Boston-area schools, including Salem State University, Smith College, and Brandeis University, began the free, 18-month program last summer with an eight-week, online skills-based course to learn the basics of AI and machine learning. Students then split into small groups in the fall to collaborate on six machine learning challenge projects presented to them by MathWorks, MIT-IBM Watson AI Lab, and Replicate. The students dedicated five hours or more each week to meet with their teams, teaching assistants, and project advisors, including convening once a month at MIT, while juggling their regular academic course load with other daily activities and responsibilities.

      The challenges gave the undergraduates the chance to help contribute to actual projects that industry organizations are working on and to put their machine learning skills to the test. Members from each organization also served as project advisors, providing encouragement and guidance to the teams throughout.

      “Students are gaining industry experience by working closely with their project advisors,” says Aude Oliva, director of strategic industry engagement at the MIT Schwarzman College of Computing and the MIT director of the MIT-IBM Watson AI Lab. “These projects will be an add-on to their machine learning portfolio that they can share as a work example when they’re ready to apply for a job in AI.”

      Over the course of 15 weeks, teams delved into large-scale, real-world datasets to train, test, and evaluate machine learning models in a variety of contexts.

      In December, the students celebrated the fruits of their labor at a showcase event held at MIT in which the six teams gave final presentations on their AI projects. The projects not only allowed the students to build up their AI and machine learning experience, it helped to “improve their knowledge base and skills in presenting their work to both technical and nontechnical audiences,” Oliva says.

      For a project on traffic data analysis, students got trained on MATLAB, a programming and numeric computing platform developed by MathWorks, to create a model that enables decision-making in autonomous driving by predicting future vehicle trajectories. “It’s important to realize that AI is not that intelligent. It’s only as smart as you make it and that’s exactly what we tried to do,” said Brandeis University student Srishti Nautiyal as she introduced her team’s project to the audience. With companies already making autonomous vehicles from planes to trucks a reality, Nautiyal, a physics and mathematics major, shared that her team was also highly motivated to consider the ethical issues of the technology in their model for the safety of passengers, drivers, and pedestrians.

      Using census data to train a model can be tricky because they are often messy and full of holes. In a project on algorithmic fairness for the MIT-IBM Watson AI Lab, the hardest task for the team was having to clean up mountains of unorganized data in a way where they could still gain insights from them. The project — which aimed to create demonstration of fairness applied on a real dataset to evaluate and compare effectiveness of different fairness interventions and fair metric learning techniques — could eventually serve as an educational resource for data scientists interested in learning about fairness in AI and using it in their work, as well as to promote the practice of evaluating the ethical implications of machine learning models in industry.

      Other challenge projects included an ML-assisted whiteboard for nontechnical people to interact with ready-made machine learning models, and a sign language recognition model to help disabled people communicate with others. A team that worked on a visual language app set out to include over 50 languages in their model to increase access for the millions of people that are visually impaired throughout the world. According to the team, similar apps on the market currently only offer up to 23 languages. 

      Throughout the semester, students persisted and demonstrated grit in order to cross the finish line on their projects. With the final presentations marking the conclusion of the fall semester, students will return to MIT in the spring to continue their Break Through Tech AI journey to tackle another round of AI projects. This time, the students will work with Google on new machine learning challenges that will enable them to hone their AI skills even further with an eye toward launching a successful career in AI. More