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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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      Meet the 2023-24 Accenture Fellows

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      The MIT and Accenture Convergence Initiative for Industry and Technology has selected five new research fellows for 2023-24. Now in its third year, the initiative underscores the ways in which industry and research can collaborate to spur technological innovation.

      Through its partnership with the School of Engineering, Accenture provides five annual fellowships awarded to graduate students with the aim of generating powerful new insights on the convergence of business and technology with the potential to transform society. The 2023-24 fellows will conduct research in areas including artificial intelligence, sustainability, and robotics.

      The 2023-24 Accenture Fellows are:

      Yiyue Luo

      Yiyue Luo is a PhD candidate who is developing innovative integrations of tactile sensing and haptics, interactive sensing and AI, digital fabrication, and smart wearables. Her work takes advantage of recent advances in digital manufacturing and AI, and the convergence in advanced sensing and actuation mechanisms, scalable digital manufacturing, and emerging computational techniques, with the goal of creating novel sensing and actuation devices that revolutionize interactions between people and their environments. In past projects, Luo has developed tactile sensing apparel including socks, gloves, and vests, as well as a workflow for computationally designing and digitally fabricating soft textiles-based pneumatic actuators. With the support of an Accenture Fellowship, she will advance her work of combining sensing and actuating devices and explore the development of haptic devices that simulate tactile cues captured by tactile sensors. Her ultimate aim is to build a scalable, textile-based, closed-loop human-machine interface. Luo’s research holds exciting potential to advance ground-breaking applications for smart textiles, health care, artificial and virtual reality, human-machine interactions, and robotics.

      Zanele Munyikwa is a PhD candidate whose research explores foundation models, a class of models that forms the basis of transformative general-purpose technologies (GPTs) such as GPT4. An Accenture Fellowship will enable Munyikwa to conduct research aimed at illuminating the current and potential impact of foundation models (including large language models) on work and tasks common to “high-skilled” knowledge workers in industries such as marketing, legal services, and medicine, in which foundation models are expected to have significant economic and social impacts. A primary goal of her project is to observe the impact of AI augmentation on tasks like copywriting and long-form writing. A second aim is to explore two primary ways that foundation models are driving the convergence of creative and technological industries, namely: reducing the cost of content generation and enabling the development of tools and platforms for education and training. Munyikwa’s work has important implications for the use of foundation models in many fields, from health care and education to legal services, business, and technology.

      Michelle Vaccaro is a PhD candidate in social engineering systems whose research explores human-AI collaboration with the goals of developing a deeper understanding of AI-based technologies (including ChatGPT and DALL-E), evaluating their performance and evolution, and steering their development toward societally beneficial applications, like climate change mitigation. An Accenture Fellowship will support Vaccaro’s current work toward two key objectives: identifying synergies between humans and AI-based software to help design human-AI systems that address persistent problems better than existing approaches; and investigating applications of human-AI collaboration for forecasting technological change, specifically for renewable energy technologies. By integrating the historically distinct domains of AI, systems engineering, and cognitive science with a wide range of industries, technical fields, and social applications, Vaccaro’s work has the potential to advance individual and collective productivity and creativity in all these areas.

      Chonghuan Wang is a PhD candidate in computational science and engineering whose research employs statistical learning, econometrics theory, and experimental design to create efficient, reliable, and sustainable field experiments in various domains. In his current work, Wang is applying statistical learning techniques such as online learning and bandit theory to test the effectiveness of new treatments, vaccinations, and health care interventions. With the support of an Accenture Fellowship, he will design experiments with the specific aim of understanding the trade-off between the loss of a patient’s welfare and the accuracy of estimating the treatment effect. The results of this research could help to save lives and contain disease outbreaks during pandemics like Covid-19. The benefits of enhanced experiment design and the collection of high-quality data extend well beyond health care; for example, these tools could help businesses optimize user engagement, test pricing impacts, and increase the usage of platforms and services. Wang’s research holds exciting potential to harness statistical learning, econometrics theory, and experimental design in support of strong businesses and the greater social good.

      Aaron Michael West Jr. is a PhD candidate whose research seeks to enhance our knowledge of human motor control and robotics. His work aims to advance rehabilitation technologies and prosthetic devices, as well as improve robot dexterity. His previous work has yielded valuable insights into the human ability to extract information solely from visual displays. Specifically, he demonstrated humans’ ability to estimate stiffness based solely on the visual observation of motion. These insights could advance the development of software applications with the same capability (e.g., using machine learning methods applied to video data) and may enable roboticists to develop enhanced motion control such that a robot’s intention is perceivable by humans. An Accenture Fellowship will enable West to continue this work, as well as new investigations into the functionality of the human hand to aid in the design of a prosthetic hand that better replicates human dexterity. By advancing understandings of human bio- and neuro-mechanics, West’s work has the potential to support major advances in robotics and rehabilitation technologies, with profound impacts on human health and well-being. More

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      M’Care and MIT students join forces to improve child health in Nigeria

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      Through a collaboration between M’Care, a 2021 Health Security and Pandemics Solver team, and students from MIT, the landscape of child health care in Nigeria could undergo a transformative change, wherein the power of data is harnessed to improve child health outcomes in economically disadvantaged communities. 

      M’Care is a mobile application of Promane and Promade Limited, developed by Opeoluwa Ashimi, which gives community health workers in Nigeria real-time diagnostic and treatment support. The application also creates a dashboard that is available to government health officials to help identify disease trends and deploy timely interventions. As part of its work, M’Care is working to mitigate malnutrition by providing micronutrient powder, vitamin A, and zinc to children below the age of 5. To help deepen its impact, Ashimi decided to work with students in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) course 6.S897 (Machine Learning for Healthcare) — instructed by professors Peter Szolovits and Manolis Kellis — to leverage data in order to improve nutrient delivery to children across Nigeria. The collaboration also enabled students to see real-world applications for data analysis in the health care space.

      A meeting of minds: M’Care, MIT, and national health authorities

      “Our primary goal for collaborating with the ML for Health team was to spot the missing link in the continuum of care. With over 1 million cumulative consultations that qualify for a continuum of care evaluation, it was important to spot why patients could be lost to followup, prevent this, and ensure completion of care to successfully address the health needs of our patients,” says Ashimi, founder and CEO of M’Care.

      In May 2023, Ashimi attended a meeting that brought together key national stakeholders, including the representatives of the National Ministry of Health in Nigeria. This gathering served as a platform to discuss the profound impact of M’Care’s and ML for Health team’s collaboration — bolstered by data analysis provided on dosage regimens and a child’s age to enhance continuum of care with its attendant impact on children’s health, particularly in relation to brain development with regards to the use of essential micronutrients. The data analyzed by the students using ML methods that were shared during the meeting provided strong supporting evidence to individualize dosage regimens for children based on their age in months for the ANRIN project — a national nutrition project supported by the World Bank — as well as policy decisions to extend months of coverage for children, redefining health care practices in Nigeria.

      MIT students drive change by harnessing the power of data

      At the heart of this collaboration lies the contribution of MIT students. Armed with their dedication and skill in data analysis and machine learning, they played a pivotal role in helping M’Care analyze their data and prepare for their meeting with the Ministry of Health. Their most significant findings included ways to identify patients at risk of not completing their full course of micronutrient powder and/or vitamin A, and identifying gaps in M’Care’s data, such as postdated delivery dates and community demographics. These findings are already helping M’Care better plan its resources and adjust the scope of its program to ensure more children complete the intervention.

      Darcy Kim, an undergraduate at Wellesley College studying math and computer science, who is cross-registered for the MIT machine learning course, expresses enthusiasm about the practical applications found within the project: “To me, data and math is storytelling, and the story is why I love studying it. … I learned that data exploration involves asking questions about how the data is collected, and that surprising patterns that arise often have a qualitative explanation. Impactful research requires radical collaboration with the people the research intends to help. Otherwise, these qualitative explanations get lost in the numbers.”

      Joyce Luo, a first-year operations research PhD student at the Operations Research Center at MIT, shares similar thoughts about the project: “I learned the importance of understanding the context behind data to figure out what kind of analysis might be most impactful. This involves being in frequent contact with the company or organization who provides the data to learn as much as you can about how the data was collected and the people the analysis could help. Stepping back and looking at the bigger picture, rather than just focusing on accuracy or metrics, is extremely important.”

      Insights to implementation: A new era for micronutrient dosing

      As a direct result of M’Care’s collaboration with MIT, policymakers revamped the dosing scheme for essential micronutrient administration for children in Nigeria to prevent malnutrition. M’Care and MIT’s data analysis unearthed critical insights into the limited frequency of medical visits caused by late-age enrollment. 

      “One big takeaway for me was that the data analysis portion of the project — doing a deep dive into the data; understanding, analyzing, visualizing, and summarizing the data — can be just as important as building the machine learning models. M’Care shared our data analysis with the National Ministry of Health, and the insights from it drove them to change their dosing scheme and schedule for delivering micronutrient powder to young children. This really showed us the value of understanding and knowing your data before modeling,” shares Angela Lin, a second-year PhD student at the Operations Research Center.

      Armed with this knowledge, policymakers are eager to develop an optimized dosing scheme that caters to the unique needs of children in disadvantaged communities, ensuring maximum impact on their brain development and overall well-being.

      Siddharth Srivastava, M’Care’s corporate technology liaison, shares his gratitude for the MIT student’s input. “Collaborating with enthusiastic and driven students was both empowering and inspiring. Each of them brought unique perspectives and technical skills to the table. Their passion for applying machine learning to health care was evident in their unwavering dedication and proactive approach to problem-solving.”

      Forging a path to impact

      The collaboration between M’Care and MIT exemplifies the remarkable achievements that arise when academia, innovative problem-solvers, and policy authorities unite. By merging academic rigor with real-world expertise, this partnership has the potential to revolutionize child health care not only in Nigeria but also in similar contexts worldwide.

      “I believe applying innovative methods of machine learning, data gathering, instrumentation, and planning to real problems in the developing world can be highly effective for those countries and highly motivating for our students. I was happy to have such a project in our class portfolio this year and look forward to future opportunities,” says Peter Szolovits, professor of computer science and engineering at MIT.

      By harnessing the power of data, innovation, and collective expertise, this collaboration between M’Care and MIT has the potential to improve equitable child health care in Nigeria. “It has been so fulfilling to see how our team’s work has been able to create even the smallest positive impact in such a short period of time, and it has been amazing to work with a company like Promane and Promade Limited that is so knowledgeable and caring for the communities that they serve,” shares Elizabeth Whittier, a second-year PhD electrical engineering student at MIT. 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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      Building a playbook for elite-level sports

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

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

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

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

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

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

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

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

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

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

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      Bringing the social and ethical responsibilities of computing to the forefront

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

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

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

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

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

      Showcasing a diversity of work

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

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

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

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

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

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

      Improving the algorithms of tomorrow

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

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

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

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

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

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

      The role of ethics in computing education

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

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

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

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

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

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

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      MIT PhD students honored for their work to solve critical issues in water and food

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      In 2017, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) initiated the J-WAFS Fellowship Program for outstanding MIT PhD students working to solve humankind’s water-related challenges. Since then, J-WAFS has awarded 18 fellowships to students who have gone on to create innovations like a pump that can maximize energy efficiency even with changing flow rates, and a low-cost water filter made out of sapwood xylem that has seen real-world use in rural India. Last year, J-WAFS expanded eligibility to students with food-related research. The 2022 fellows included students working on micronutrient deficiency and plastic waste from traditional food packaging materials. 

      Today, J-WAFS has announced the award of the 2023-24 fellowships to Gokul Sampath and Jie Yun. A doctoral student in the Department of Urban Studies and planning, Sampath has been awarded the Rasikbhai L. Meswani Fellowship for Water Solutions, which is supported through a generous gift from Elina and Nikhil Meswani and family. Yun, who is in the Department of Civil and Environmental Engineering, received a J-WAFS Fellowship for Water and Food Solutions, which is funded by the J-WAFS Research Affiliate Program. Currently, Xylem, Inc. and GoAigua are J-WAFS’ Research Affiliate companies. A review committee comprised of MIT faculty and staff selected Sampath and Yun from a competitive field of outstanding graduate students working in water and food who were nominated by their faculty advisors. Sampath and Yun will receive one academic semester of funding, along with opportunities for networking and mentoring to advance their research.

      “Both Yun and Sampath have demonstrated excellence in their research,” says J-WAFS executive director Renee J. Robins. “They also stood out in their communication skills and their passion to work on issues of agricultural sustainability and resilience and access to safe water. We are so pleased to have them join our inspiring group of J-WAFS fellows,” she adds.

      Using behavioral health strategies to address the arsenic crisis in India and Bangladesh

      Gokul Sampath’s research centers on ways to improve access to safe drinking water in developing countries. A PhD candidate in the International Development Group in the Department of Urban Studies and Planning, his current work examines the issue of arsenic in drinking water sources in India and Bangladesh. In Eastern India, millions of shallow tube wells provide rural households a personal water source that is convenient, free, and mostly safe from cholera. Unfortunately, it is now known that one-in-four of these wells is contaminated with naturally occurring arsenic at levels dangerous to human health. As a result, approximately 40 million people across the region are at elevated risk of cancer, stroke, and heart disease from arsenic consumed through drinking water and cooked food. 

      Since the discovery of arsenic in wells in the late 1980s, governments and nongovernmental organizations have sought to address the problem in rural villages by providing safe community water sources. Yet despite access to safe alternatives, many households still consume water from their contaminated home wells. Sampath’s research seeks to understand the constraints and trade-offs that account for why many villagers don’t collect water from arsenic-safe government wells in the village, even when they know their own wells at home could be contaminated.

      Before coming to MIT, Sampath received a master’s degree in Middle East, South Asian, and African studies from Columbia University, as well as a bachelor’s degree in microbiology and history from the University of California at Davis. He has long worked on water management in India, beginning in 2015 as a Fulbright scholar studying households’ water source choices in arsenic-affected areas of the state of West Bengal. He also served as a senior research associate with the Abdul Latif Jameel Poverty Action Lab, where he conducted randomized evaluations of market incentives for groundwater conservation in Gujarat, India. Sampath’s advisor, Bishwapriya Sanyal, the Ford International Professor of Urban Development and Planning at MIT, says Sampath has shown “remarkable hard work and dedication.” In addition to his classes and research, Sampath taught the department’s undergraduate Introduction to International Development course, for which he received standout evaluations from students.

      This summer, Sampath will travel to India to conduct field work in four arsenic-affected villages in West Bengal to understand how social influence shapes villagers’ choices between arsenic-safe and unsafe water sources. Through longitudinal surveys, he hopes to connect data on the social ties between families in villages and the daily water source choices they make. Exclusionary practices in Indian village communities, especially the segregation of water sources on the basis of caste and religion, has long been suspected to be a barrier to equitable drinking water access in Indian villages. Yet despite this, planners seeking to expand safe water access in diverse Indian villages have rarely considered the way social divisions within communities might be working against their efforts. Sampath hopes to test whether the injunctive norms enabled by caste ties constrain villagers’ ability to choose the safest water source among those shared within the village. When he returns to MIT in the fall, he plans to dive into analyzing his survey data and start work on a publication.

      Understanding plant responses to stress to improve crop drought resistance and yield

      Plants, including crops, play a fundamental role in Earth’s ecosystems through their effects on climate, air quality, and water availability. At the same time, plants grown for agriculture put a burden on the environment as they require energy, irrigation, and chemical inputs. Understanding plant/environment interactions is becoming more and more important as intensifying drought is straining agricultural systems. Jie Yun, a PhD student in the Department of Civil and Environmental Engineering, is studying plant response to drought stress in the hopes of improving agricultural sustainability and yield under climate change.  Yun’s research focuses on genotype-by-environment interaction (GxE.) This relates to the observation that plant varieties respond to environmental changes differently. The effects of GxE in crop breeding can be exploited because differing environmental responses among varieties enables breeders to select for plants that demonstrate high stress-tolerant genotypes under particular growing conditions. Yun bases her studies on Brachypodium, a model grass species related to wheat, oat, barley, rye, and perennial forage grasses. By experimenting with this species, findings can be directly applied to cereal and forage crop improvement. For the first part of her thesis, Yun collaborated with Professor Caroline Uhler’s group in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. Uhler’s computational tools helped Yun to evaluate gene regulatory networks and how they relate to plant resilience and environmental adaptation. This work will help identify the types of genes and pathways that drive differences in drought stress response among plant varieties.  David Des Marais, the Cecil and Ida Green Career Development Professor in the Department of Civil and Environmental Engineering, is Yun’s advisor. He notes, “throughout Jie’s time [at MIT] I have been struck by her intellectual curiosity, verging on fearlessness.” When she’s not mentoring undergraduate students in Des Marais’ lab, Yun is working on the second part of her project: how carbon allocation in plants and growth is affected by soil drying. One result of this work will be to understand which populations of plants harbor the necessary genetic diversity to adapt or acclimate to climate change. Another likely impact is identifying targets for the genetic improvement of crop species to increase crop yields with less water supply. Growing up in China, Yun witnessed environmental issues springing from the development of the steel industry, which caused contamination of rivers in her hometown. On one visit to her aunt’s house in rural China, she learned that water pollution was widespread after noticing wastewater was piped outside of the house into nearby farmland without being treated. These experiences led Yun to study water supply and sewage engineering for her undergraduate degree at Shenyang Jianzhu University. She then went on to complete a master’s program in civil and environmental engineering at Carnegie Mellon University. It was there that Yun discovered a passion for plant-environment interactions; during an independent study on perfluorooctanoic sulfonate, she realized the amazing ability of plants to adapt to environmental changes, toxins, and stresses. Her goal is to continue researching plant and environment interactions and to translate the latest scientific findings into applications that can improve food security. More

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      Meet the 2022-23 Accenture Fellows

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      Launched in October 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology can collaborate to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. To that end, Accenture has once again awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity, and gender.This year’s Accenture Fellows work across research areas including telemonitoring, human-computer interactions, operations research,  AI-mediated socialization, and chemical transformations. Their research covers a wide array of projects, including designing low-power processing hardware for telehealth applications; applying machine learning to streamline and improve business operations; improving mental health care through artificial intelligence; and using machine learning to understand the environmental and health consequences of complex chemical reactions.As part of the application process, student nominations were invited from each unit within the School of Engineering, as well as from the Institute’s four other schools and the MIT Schwarzman College of Computing. Five exceptional students were selected as fellows for the initiative’s third year.Drew Buzzell is a doctoral candidate in electrical engineering and computer science whose research concerns telemonitoring, a fast-growing sphere of telehealth in which information is collected through internet-of-things (IoT) connected devices and transmitted to the cloud. Currently, the high volume of information involved in telemonitoring — and the time and energy costs of processing it — make data analysis difficult. Buzzell’s work is focused on edge computing, a new computing architecture that seeks to address these challenges by managing data closer to the source, in a distributed network of IoT devices. Buzzell earned his BS in physics and engineering science and his MS in engineering science from the Pennsylvania State University.

      Mengying (Cathy) Fang is a master’s student in the MIT School of Architecture and Planning. Her research focuses on augmented reality and virtual reality platforms. Fang is developing novel sensors and machine components that combine computation, materials science, and engineering. Moving forward, she will explore topics including soft robotics techniques that could be integrated with clothes and wearable devices and haptic feedback in order to develop interactions with digital objects. Fang earned a BS in mechanical engineering and human-computer interaction from Carnegie Mellon University.

      Xiaoyue Gong is a doctoral candidate in operations research at the MIT Sloan School of Management. Her research aims to harness the power of machine learning and data science to reduce inefficiencies in the operation of businesses, organizations, and society. With the support of an Accenture Fellowship, Gong seeks to find solutions to operational problems by designing reinforcement learning methods and other machine learning techniques to embedded operational problems. Gong earned a BS in honors mathematics and interactive media arts from New York University.

      Ruby Liu is a doctoral candidate in medical engineering and medical physics. Their research addresses the growing pandemic of loneliness among older adults, which leads to poor health outcomes and presents particularly high risks for historically marginalized people, including members of the LGBTQ+ community and people of color. Liu is designing a network of interconnected AI agents that foster connections between user and agent, offering mental health care while strengthening and facilitating human-human connections. Liu received a BS in biomedical engineering from Johns Hopkins University.

      Joules Provenzano is a doctoral candidate in chemical engineering. Their work integrates machine learning and liquid chromatography-high resolution mass spectrometry (LC-HRMS) to improve our understanding of complex chemical reactions in the environment. As an Accenture Fellow, Provenzano will build upon recent advances in machine learning and LC-HRMS, including novel algorithms for processing real, experimental HR-MS data and new approaches in extracting structure-transformation rules and kinetics. Their research could speed the pace of discovery in the chemical sciences and benefits industries including oil and gas, pharmaceuticals, and agriculture. Provenzano earned a BS in chemical engineering and international and global studies from the Rochester Institute of Technology. More