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    Four from MIT receive NIH New Innovator Awards for 2022

    The National Institutes of Health (NIH) has awarded grants to four MIT faculty members as part of its High-Risk, High-Reward Research program.

    The program supports unconventional approaches to challenges in biomedical, behavioral, and social sciences. Each year, NIH Director’s Awards are granted to program applicants who propose high-risk, high-impact research in areas relevant to the NIH’s mission. In doing so, the NIH encourages innovative proposals that, due to their inherent risk, might struggle in the traditional peer-review process.

    This year, Lindsay Case, Siniša Hrvatin, Deblina Sarkar, and Caroline Uhler have been chosen to receive the New Innovator Award, which funds exceptionally creative research from early-career investigators. The award, which was established in 2007, supports researchers who are within 10 years of their final degree or clinical residency and have not yet received a research project grant or equivalent NIH grant.

    Lindsay Case, the Irwin and Helen Sizer Department of Biology Career Development Professor and an extramural member of the Koch Institute for Integrative Cancer Research, uses biochemistry and cell biology to study the spatial organization of signal transduction. Her work focuses on understanding how signaling molecules assemble into compartments with unique biochemical and biophysical properties to enable cells to sense and respond to information in their environment. Earlier this year, Case was one of two MIT assistant professors named as Searle Scholars.

    Siniša Hrvatin, who joined the School of Science faculty this past winter, is an assistant professor in the Department of Biology and a core member at the Whitehead Institute for Biomedical Research. He studies how animals and cells enter, regulate, and survive states of dormancy such as torpor and hibernation, aiming to harness the potential of these states therapeutically.

    Deblina Sarkar is an assistant professor and AT&T Career Development Chair Professor at the MIT Media Lab​. Her research combines the interdisciplinary fields of nanoelectronics, applied physics, and biology to invent disruptive technologies for energy-efficient nanoelectronics and merge such next-generation technologies with living matter to create a new paradigm for life-machine symbiosis. Her high-risk, high-reward proposal received the rare perfect impact score of 10, which is the highest score awarded by NIH.

    Caroline Uhler is a professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. In addition, she is a core institute member at the Broad Institute of MIT and Harvard, where she co-directs the Eric and Wendy Schmidt Center. By combining machine learning, statistics, and genomics, she develops representation learning and causal inference methods to elucidate gene regulation in health and disease.

    The High-Risk, High-Reward Research program is supported by the NIH Common Fund, which oversees programs that pursue major opportunities and gaps in biomedical research that require collaboration across NIH Institutes and Centers. In addition to the New Innovator Award, the NIH also issues three other awards each year: the Pioneer Award, which supports bold and innovative research projects with unusually broad scientific impact; the Transformative Research Award, which supports risky and untested projects with transformative potential; and the Early Independence Award, which allows especially impressive junior scientists to skip the traditional postdoctoral training program to launch independent research careers.

    This year, the High-Risk, High-Reward Research program is awarding 103 awards, including eight Pioneer Awards, 72 New Innovator Awards, nine Transformative Research Awards, and 14 Early Independence Awards. These 103 awards total approximately $285 million in support from the institutes, centers, and offices across NIH over five years. “The science advanced by these researchers is poised to blaze new paths of discovery in human health,” says Lawrence A. Tabak DDS, PhD, who is performing the duties of the director of NIH. “This unique cohort of scientists will transform what is known in the biological and behavioral world. We are privileged to support this innovative science.” More

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    Empowering Cambridge youth through data activism

    For over 40 years, the Mayor’s Summer Youth Employment Program (MSYEP, or the Mayor’s Program) in Cambridge, Massachusetts, has been providing teenagers with their first work experience, but 2022 brought a new offering. Collaborating with MIT’s Personal Robots research group (PRG) and Responsible AI for Social Empowerment and Education (RAISE) this summer, MSYEP created a STEAM-focused learning site at the Institute. Eleven students joined the program to learn coding and programming skills through the lens of “Data Activism.”

    MSYEP’s partnership with MIT provides an opportunity for Cambridge high schoolers to gain exposure to more pathways for their future careers and education. The Mayor’s Program aims to respect students’ time and show the value of their work, so participants are compensated with an hourly wage as they learn workforce skills at MSYEP worksites. In conjunction with two ongoing research studies at MIT, PRG and RAISE developed the six-week Data Activism curriculum to equip students with critical-thinking skills so they feel prepared to utilize data science to challenge social injustice and empower their community.

    Rohan Kundargi, K-12 Community Outreach Administrator for MIT Office of Government and Community Relations (OGCR), says, “I see this as a model for a new type of partnership between MIT and Cambridge MSYEP. Specifically, an MIT research project that involves students from Cambridge getting paid to learn, research, and develop their own skills!”

    Cross-Cambridge collaboration

    Cambridge’s Office of Workforce Development initially contacted MIT OGCR about hosting a potential MSYEP worksite that taught Cambridge teens how to code. When Kundargi reached out to MIT pK-12 collaborators, MIT PRG’s graduate research assistant Raechel Walker proposed the Data Activism curriculum. Walker defines “data activism” as utilizing data, computing, and art to analyze how power operates in the world, challenge power, and empathize with people who are oppressed.

    Walker says, “I wanted students to feel empowered to incorporate their own expertise, talents, and interests into every activity. In order for students to fully embrace their academic abilities, they must remain comfortable with bringing their full selves into data activism.”

    As Kundargi and Walker recruited students for the Data Activism learning site, they wanted to make sure the cohort of students — the majority of whom are individuals of color — felt represented at MIT and felt they had the agency for their voice to be heard. “The pioneers in this field are people who look like them,” Walker says, speaking of well-known data activists Timnit Gebru, Rediet Abebe, and Joy Buolamwini.

    When the program began this summer, some of the students were not aware of the ways data science and artificial intelligence exacerbate systemic oppression in society, or some of the tools currently being used to mitigate those societal harms. As a result, Walker says, the students wanted to learn more about discriminatory design in every aspect of life. They were also interested in creating responsible machine learning algorithms and AI fairness metrics.

    A different side of STEAM

    The development and execution of the Data Activism curriculum contributed to Walker’s and postdoc Xiaoxue Du’s respective research at PRG. Walker is studying AI education, specifically creating and teaching data activism curricula for minoritized communities. Du’s research explores processes, assessments, and curriculum design that prepares educators to use, adapt, and integrate AI literacy curricula. Additionally, her research targets how to leverage more opportunities for students with diverse learning needs.

    The Data Activism curriculum utilizes a “libertatory computing” framework, a term Walker coined in her position paper with Professor Cynthia Breazeal, director of MIT RAISE, dean for digital learning, and head of PRG, and Eman Sherif, a then-undergraduate researcher from University of California at San Diego, titled “Liberty Computing for African American Students.” This framework ensures that students, especially minoritized students, acquire a sound racial identity, critical consciousness, collective obligation, liberation centered academic/achievement identity, as well as the activism skills to use computing to transform a multi-layered system of barriers in which racism persists. Walker says, “We encouraged students to demonstrate competency in every pillar because all of the pillars are interconnected and build upon each other.”

    Walker developed a series of interactive coding and project-based activities that focused on understanding systemic racism, utilizing data science to analyze systemic oppression, data drawing, responsible machine learning, how racism can be embedded into AI, and different AI fairness metrics.

    This was the students’ first time learning how to create data visualizations using the programming language Python and the data analysis tool Pandas. In one project meant to examine how different systems of oppression can affect different aspects of students’ own identities, students created datasets with data from their respective intersectional identities. Another activity highlighted African American achievements, where students analyzed two datasets about African American scientists, activists, artists, scholars, and athletes. Using the data visualizations, students then created zines about the African Americans who inspired them.

    RAISE hired Olivia Dias, Sophia Brady, Lina Henriquez, and Zeynep Yalcin through the MIT Undergraduate Research Opportunity Program (UROP) and PRG hired freelancer Matt Taylor to work with Walker on developing the curriculum and designing interdisciplinary experience projects. Walker and the four undergraduate researchers constructed an intersectional data analysis activity about different examples of systemic oppression. PRG also hired three high school students to test activities and offer insights about making the curriculum engaging for program participants. Throughout the program, the Data Activism team taught students in small groups, continually asked students how to improve each activity, and structured each lesson based on the students’ interests. Walker says Dias, Brady, Henriquez, and Yalcin were invaluable to cultivating a supportive classroom environment and helping students complete their projects.

    Cambridge Rindge and Latin School senior Nina works on her rubber block stamp that depicts the importance of representation in media and greater representation in the tech industry.

    Photo: Katherine Ouellette

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    Student Nina says, “It’s opened my eyes to a different side of STEM. I didn’t know what ‘data’ meant before this program, or how intersectionality can affect AI and data.” Before MSYEP, Nina took Intro to Computer Science and AP Computer Science, but she has been coding since Girls Who Code first sparked her interest in middle school. “The community was really nice. I could talk with other girls. I saw there needs to be more women in STEM, especially in coding.” Now she’s interested in applying to colleges with strong computer science programs so she can pursue a coding-related career.

    From MSYEP to the mayor’s office

    Mayor Sumbul Siddiqui visited the Data Activism learning site on Aug. 9, accompanied by Breazeal. A graduate of MSYEP herself, Siddiqui says, “Through hands-on learning through computer programming, Cambridge high school students have the unique opportunity to see themselves as data scientists. Students were able learn ways to combat discrimination that occurs through artificial intelligence.” In an Instagram post, Siddiqui also said, “I had a blast visiting the students and learning about their projects.”

    Students worked on an activity that asked them to envision how data science might be used to support marginalized communities. They transformed their answers into block-printed T-shirt designs, carving pictures of their hopes into rubber block stamps. Some students focused on the importance of data privacy, like Jacob T., who drew a birdcage to represent data stored and locked away by third party apps. He says, “I want to open that cage and restore my data to myself and see what can be done with it.”

    The subject of Cambridge Community Charter School student Jacob T.’s project was the importance of data privacy. For his T-shirt design, he drew a birdcage to represent data stored and locked away by third party apps. (From right to left:) Breazeal, Jacob T. Kiki, Raechel Walker, and Zeynep Yalcin.

    Photo: Katherine Ouellette

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    Many students wanted to see more representation in both the media they consume and across various professional fields. Nina talked about the importance of representation in media and how that could contribute to greater representation in the tech industry, while Kiki talked about encouraging more women to pursue STEM fields. Jesmin said, “I wanted to show that data science is accessible to everyone, no matter their origin or language you speak. I wrote ‘hello’ in Bangla, Arabic, and English, because I speak all three languages and they all resonate with me.”

    Student Jesmin (left) explains the concept of her T-shirt design to Mayor Siddiqui. She wants data science to be accessible to everyone, no matter their origin or language, so she drew a globe and wrote ‘hello’ in the three languages she speaks: Bangla, Arabic, and English.

    Photo: Katherine Ouellette

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    “Overall, I hope the students continue to use their data activism skills to re-envision a society that supports marginalized groups,” says Walker. “Moreover, I hope they are empowered to become data scientists and understand how their race can be a positive part of their identity.” More

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    MIT welcomes eight MLK Visiting Professors and Scholars for 2022-23

    From space traffic to virus evolution, community journalism to hip-hop, this year’s cohort in the Martin Luther King Jr. (MLK) Visiting Professors and Scholars Program will power an unprecedented range of intellectual pursuits during their time on the MIT campus. 

    “MIT is so fortunate to have this group of remarkable individuals join us,” says Institute Community and Equity Officer John Dozier. “They bring a range and depth of knowledge to share with our students and faculty, and we look forward to working with them to build a stronger sense of community across the Institute.”

    Since its inception in 1990, the MLK Scholars Program has hosted more than 135 visiting professors, practitioners, and intellectuals who enhance and enrich the MIT community through their engagement with students and faculty. The program, which honors the life and legacy of MLK by increasing the presence and recognizing the contributions of underrepresented scholars, is supported by the Office of the Provost with oversight from the Institute Community and Equity Office. 

    In spring 2022, MIT President Rafael Reif committed to MIT to adding two new positions in the MLK Visiting Scholars Program, including an expert in Native American studies. Those additional positions will be filled in the coming year.  

    The 2022-23 MLK Scholars:

    Daniel Auguste is an assistant professor in the Department of Sociology at Florida Atlantic University and is hosted by Roberto Fernandez in MIT Sloan School of Management. Auguste’s research interests include social inequalities in entrepreneurship development. During his visit, Auguste will study the impact of education debt burden and wealth inequality on business ownership and success, and how these consequences differ by race and ethnicity.

    Tawanna Dillahunt is an associate professor in the School of Information at the University of Michigan, where she also holds an appointment with the electrical engineering and computer science department. Catherine D’Ignazio in the Department of Urban Studies and Planning and Fotini Christia in the Institute for Data, Systems, and Society are her faculty hosts. Dillahunt’s scholarship focuses on equitable and inclusive computing. She identifies technological opportunities and implements tools to address and alleviate employment challenges faced by marginalized people. Dillahunt’s visiting appointment begins in September 2023.

    Javit Drake ’94 is a principal scientist in modeling and simulation and measurement sciences at Proctor & Gamble. His faculty host is Fikile Brushett in the Department of Chemical Engineering. An industry researcher with electrochemical energy expertise, Drake is a Course 10 (chemical engineering) alumnus, repeat lecturer, and research affiliate in the department. During his visit, he will continue to work with the Brushett Research Group to deepen his research and understanding of battery technologies while he innovates from those discoveries.

    Eunice Ferreira is an associate professor in the Department of Theater at Skidmore College and is hosted by Claire Conceison in Music and Theater Arts. This fall, Ferreira will teach “Black Theater Matters,” a course where students will explore performance and the cultural production of Black intellectuals and artists on Broadway and in local communities. Her upcoming book projects include “Applied Theatre and Racial Justice: Radical Imaginings for Just Communities” (forthcoming from Routledge) and “Crioulo Performance: Remapping Creole and Mixed Race Theatre” (forthcoming from Vanderbilt University Press). 

    Wasalu Jaco, widely known as Lupe Fiasco, is a rapper, record producer, and entrepreneur. He will be co-hosted by Nick Montfort of Comparative Media Studies/Writing and Mary Fuller of Literature. Jaco’s interests lie in the nexus of rap, computing, and activism. As a former visiting artist in MIT’s Center for Art, Science and Technology (CAST), he will leverage existing collaborations and participate in digital media and art research projects that use computing to explore novel questions related to hip-hop and rap. In addition to his engagement in cross-departmental projects, Jaco will teach a spring course on rap in the media and social contexts.

    Moribah Jah is an associate professor in the Aerospace Engineering and Engineering Mechanics Department at the University of Texas at Austin. He is hosted by Danielle Wood in Media Arts and Sciences and the Department of Aeronautics and Astronautics, and Richard Linares in the Department of Aeronautics and Astronautics. Jah’s research interests include space sustainability and space traffic management; as a visiting scholar, he will develop and strengthen a joint MIT/UT-Austin research program to increase resources and visibility of space sustainability. Jah will also help host the AeroAstro Rising Stars symposium, which highlights graduate students, postdocs, and early-career faculty from backgrounds underrepresented in aerospace engineering. 

    Louis Massiah SM ’82 is a documentary filmmaker and the founder and director of community media of Scribe Video Center, a nonprofit organization that uses media as a tool for social change. His work focuses on empowering Black, Indigenous, and People of Color (BIPOC) filmmakers to tell the stories of/by BIPOC communities. Massiah is hosted by Vivek Bald in Creative Media Studies/Writing. Massiah’s first project will be the launch of a National Community Media Journalism Consortium, a platform to share local news on a broader scale across communities.

    Brian Nord, a scientist at Fermi National Accelerator Laboratory, will join the Laboratory for Nuclear Science, hosted by Jesse Thaler in the Department of Physics. Nord’s research interests include the connection between ethics, justice, and scientific discovery. His efforts will be aimed at introducing new insights into how we model physical systems, design scientific experiments, and approach the ethics of artificial intelligence. As a lead organizer of the Strike for Black Lives in 2020, Nord will engage with justice-oriented members of the MIT physics community to strategize actions for advocacy and activism.

    Brandon Ogbunu, an assistant professor in the Department of Ecology and Evolutionary Biology at Yale University, will be hosted by Matthew Shoulders in the Department of Chemistry. Ogbunu’s research focus is on implementing chemistry and materials science perspectives into his work on virus evolution. In addition to serving as a guest lecturer in graduate courses, he will be collaborating with the Office of Engineering Outreach Programs on their K-12 outreach and recruitment efforts.

    For more information about these scholars and the program, visit mlkscholars.mit.edu. More

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    Ocean vital signs

    Without the ocean, the climate crisis would be even worse than it is. Each year, the ocean absorbs billions of tons of carbon from the atmosphere, preventing warming that greenhouse gas would otherwise cause. Scientists estimate about 25 to 30 percent of all carbon released into the atmosphere by both human and natural sources is absorbed by the ocean.

    “But there’s a lot of uncertainty in that number,” says Ryan Woosley, a marine chemist and a principal research scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at MIT. Different parts of the ocean take in different amounts of carbon depending on many factors, such as the season and the amount of mixing from storms. Current models of the carbon cycle don’t adequately capture this variation.

    To close the gap, Woosley and a team of other MIT scientists developed a research proposal for the MIT Climate Grand Challenges competition — an Institute-wide campaign to catalyze and fund innovative research addressing the climate crisis. The team’s proposal, “Ocean Vital Signs,” involves sending a fleet of sailing drones to cruise the oceans taking detailed measurements of how much carbon the ocean is really absorbing. Those data would be used to improve the precision of global carbon cycle models and improve researchers’ ability to verify emissions reductions claimed by countries.

    “If we start to enact mitigation strategies—either through removing CO2 from the atmosphere or reducing emissions — we need to know where CO2 is going in order to know how effective they are,” says Woosley. Without more precise models there’s no way to confirm whether observed carbon reductions were thanks to policy and people, or thanks to the ocean.

    “So that’s the trillion-dollar question,” says Woosley. “If countries are spending all this money to reduce emissions, is it enough to matter?”

    In February, the team’s Climate Grand Challenges proposal was named one of 27 finalists out of the almost 100 entries submitted. From among this list of finalists, MIT will announce in April the selection of five flagship projects to receive further funding and support.

    Woosley is leading the team along with Christopher Hill, a principal research engineer in EAPS. The team includes physical and chemical oceanographers, marine microbiologists, biogeochemists, and experts in computational modeling from across the department, in addition to collaborators from the Media Lab and the departments of Mathematics, Aeronautics and Astronautics, and Electrical Engineering and Computer Science.

    Today, data on the flux of carbon dioxide between the air and the oceans are collected in a piecemeal way. Research ships intermittently cruise out to gather data. Some commercial ships are also fitted with sensors. But these present a limited view of the entire ocean, and include biases. For instance, commercial ships usually avoid storms, which can increase the turnover of water exposed to the atmosphere and cause a substantial increase in the amount of carbon absorbed by the ocean.

    “It’s very difficult for us to get to it and measure that,” says Woosley. “But these drones can.”

    If funded, the team’s project would begin by deploying a few drones in a small area to test the technology. The wind-powered drones — made by a California-based company called Saildrone — would autonomously navigate through an area, collecting data on air-sea carbon dioxide flux continuously with solar-powered sensors. This would then scale up to more than 5,000 drone-days’ worth of observations, spread over five years, and in all five ocean basins.

    Those data would be used to feed neural networks to create more precise maps of how much carbon is absorbed by the oceans, shrinking the uncertainties involved in the models. These models would continue to be verified and improved by new data. “The better the models are, the more we can rely on them,” says Woosley. “But we will always need measurements to verify the models.”

    Improved carbon cycle models are relevant beyond climate warming as well. “CO2 is involved in so much of how the world works,” says Woosley. “We’re made of carbon, and all the other organisms and ecosystems are as well. What does the perturbation to the carbon cycle do to these ecosystems?”

    One of the best understood impacts is ocean acidification. Carbon absorbed by the ocean reacts to form an acid. A more acidic ocean can have dire impacts on marine organisms like coral and oysters, whose calcium carbonate shells and skeletons can dissolve in the lower pH. Since the Industrial Revolution, the ocean has become about 30 percent more acidic on average.

    “So while it’s great for us that the oceans have been taking up the CO2, it’s not great for the oceans,” says Woosley. “Knowing how this uptake affects the health of the ocean is important as well.” More

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    Professors Elchanan Mossel and Rosalind Picard named 2021 ACM Fellows

    The Association for Computing Machinery (ACM) has named MIT professors Elchanan Mossel and Rosalind Picard as fellows for outstanding accomplishments in computing and information technology.

    The ACM Fellows program recognizes wide-ranging and fundamental contributions in areas including algorithms, computer science education, cryptography, data security and privacy, medical informatics, and mobile and networked systems, among many other areas. The accomplishments of the 2021 ACM Fellows underpin important innovations that shape the technologies we use every day.

    Elchanan Mossel

    Mossel is a professor of mathematics and a member at the Statistics and Data Science Center of the MIT Institute for Data, Systems and Society. His research in discrete functional inequalities, isoperimetry, and hypercontractivity led to the proof that Majority is Stablest and confirmed the optimality of the Goemans-Williamson MAX-CUT algorithm under the unique games conjecture from computational complexity. His work on the reconstruction problem on trees provides optimal algorithms and bounds for phylogenetic reconstruction in molecular biology and has led to sharp results in the analysis of Gibbs samplers from statistical physics and inference problems on graphs. His research has resolved open problems in computational biology, machine learning, social choice theory, and economics.Mossel received a BS from the Open University in Israel in 1992, and MS (1997) and PhD (2000) degrees in mathematics from the Hebrew University of Jerusalem. He was a postdoc at the Microsoft Research Theory Group and a Miller Fellow at University of California at Berkeley. He joined the UC Berkeley faculty in 2003 as a professor of statistics and computer science, and spent leaves as a professor at the Weizmann Institute and at the Wharton School before joining MIT in 2016 as a full professor.

    In 2020, he received the Vannevar Bush Faculty Fellowship of the U.S. Department of Defense. Other distinctions include being named a Simons Investigator in Mathematics in 2019, being selected as a fellow of the AMS, and receiving a Sloan Research Fellowship, NSF CAREER Award, and the Bergmann Memorial Award from the U.S.-Israel Binational Science Foundation.

    “I am honored by this award,” says Mossel. “It makes me realize how fortunate I’ve been, working with creative and generous colleagues, and mentoring brilliant young minds.”

    Rosalind Picard

    Picard is a scientist, engineer, author, and professor of media arts and sciences at the MIT Media Lab. She is recognized as the founder of the field of affective computing, and has carried this research forward as head of the Media Lab’s Affective Computing research group. She is also a founding faculty chair of MIT’s MindHandHeart Initiative, and a faculty member of the MIT Center for Neurobiological Engineering. Picard is an IEEE fellow, and a member of the National Academy of Engineering. 

    Picard’s inventions are in use by thousands of research teams worldwide as well as in numerous products and services. She has co-founded two companies: Affectiva (now part of Smart Eye), providing emotion AI technologies now used by more than 25 percent of the Global Fortune 500, and Empatica, providing wearable sensors and analytics to improve health. Starting from inventions by Picard and her team, Empatica created the first AI-based smart watch cleared by the FDA (in neurology for monitoring seizures), which is now helping to bring potentially lifesaving help for people with epilepsy. 

    “This award makes me think of how blessed I am to work with so many amazing people here at MIT, especially at the Media Lab,” Picard notes. “Whenever any one of us has our contributions recognized, it is also a recognition of how special a place this is.” More

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    The promise and pitfalls of artificial intelligence explored at TEDxMIT event

    Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT. 

    Attendees were entertained and challenged as they explored “the good and bad of computing,” explained CSAIL Director Professor Daniela Rus, who organized the event with John Werner, an MIT fellow and managing director of Link Ventures; MIT sophomore Lucy Zhao; and grad student Jessica Karaguesian. “As you listen to the talks today,” Rus told the audience, “consider how our world is made better by AI, and also our intrinsic responsibilities for ensuring that the technology is deployed for the greater good.”

    Rus mentioned some new capabilities that could be enabled by AI: an automated personal assistant that could monitor your sleep phases and wake you at the optimal time, as well as on-body sensors that monitor everything from your posture to your digestive system. “Intelligent assistance can help empower and augment our lives. But these intriguing possibilities should only be pursued if we can simultaneously resolve the challenges that these technologies bring,” said Rus. 

    The next speaker, CSAIL principal investigator and professor of electrical engineering and computer science Manolis Kellis, started off by suggesting what sounded like an unattainable goal — using AI to “put an end to evolution as we know it.” Looking at it from a computer science perspective, he said, what we call evolution is basically a brute force search. “You’re just exploring all of the search space, creating billions of copies of every one of your programs, and just letting them fight against each other. This is just brutal. And it’s also completely slow. It took us billions of years to get here.” Might it be possible, he asked, to speed up evolution and make it less messy?

    The answer, Kellis said, is that we can do better, and that we’re already doing better: “We’re not killing people like Sparta used to, throwing the weaklings off the mountain. We are truly saving diversity.”

    Knowledge, moreover, is now being widely shared, passed on “horizontally” through accessible information sources, he noted, rather than “vertically,” from parent to offspring. “I would like to argue that competition in the human species has been replaced by collaboration. Despite having a fixed cognitive hardware, we have software upgrades that are enabled by culture, by the 20 years that our children spend in school to fill their brains with everything that humanity has learned, regardless of which family came up with it. This is the secret of our great acceleration” — the fact that human advancement in recent centuries has vastly out-clipped evolution’s sluggish pace.

    The next step, Kellis said, is to harness insights about evolution in order to combat an individual’s genetic susceptibility to disease. “Our current approach is simply insufficient,” he added. “We’re treating manifestations of disease, not the causes of disease.” A key element in his lab’s ambitious strategy to transform medicine is to identify “the causal pathways through which genetic predisposition manifests. It’s only by understanding these pathways that we can truly manipulate disease causation and reverse the disease circuitry.” 

    Kellis was followed by Aleksander Madry, MIT professor of electrical engineering and computer science and CSAIL principal investigator, who told the crowd, “progress in AI is happening, and it’s happening fast.” Computer programs can routinely beat humans in games like chess, poker, and Go. So should we be worried about AI surpassing humans? 

    Madry, for one, is not afraid — or at least not yet. And some of that reassurance stems from research that has led him to the following conclusion: Despite its considerable success, AI, especially in the form of machine learning, is lazy. “Think about being lazy as this kind of smart student who doesn’t really want to study for an exam. Instead, what he does is just study all the past years’ exams and just look for patterns. Instead of trying to actually learn, he just tries to pass the test. And this is exactly the same way in which current AI is lazy.”

    A machine-learning model might recognize grazing sheep, for instance, simply by picking out pictures that have green grass in them. If a model is trained to identify fish from photos of anglers proudly displaying their catches, Madry explained, “the model figures out that if there’s a human holding something in the picture, I will just classify it as a fish.” The consequences can be more serious for an AI model intended to pick out malignant tumors. If the model is trained on images containing rulers that indicate the size of tumors, the model may end up selecting only those photos that have rulers in them.

    This leads to Madry’s biggest concerns about AI in its present form. “AI is beating us now,” he noted. “But the way it does it [involves] a little bit of cheating.” He fears that we will apply AI “in some way in which this mismatch between what the model actually does versus what we think it does will have some catastrophic consequences.” People relying on AI, especially in potentially life-or-death situations, need to be much more mindful of its current limitations, Madry cautioned.

    There were 10 speakers altogether, and the last to take the stage was MIT associate professor of electrical engineering and computer science and CSAIL principal investigator Marzyeh Ghassemi, who laid out her vision for how AI could best contribute to general health and well-being. But in order for that to happen, its models must be trained on accurate, diverse, and unbiased medical data.

    It’s important to focus on the data, Ghassemi stressed, because these models are learning from us. “Since our data is human-generated … a neural network is learning how to practice from a doctor. But doctors are human, and humans make mistakes. And if a human makes a mistake, and we train an AI from that, the AI will, too. Garbage in, garbage out. But it’s not like the garbage is distributed equally.”

    She pointed out that many subgroups receive worse care from medical practitioners, and members of these subgroups die from certain conditions at disproportionately high rates. This is an area, Ghassemi said, “where AI can actually help. This is something we can fix.” Her group is developing machine-learning models that are robust, private, and fair. What’s holding them back is neither algorithms nor GPUs. It’s data. Once we collect reliable data from diverse sources, Ghassemi added, we might start reaping the benefits that AI can bring to the realm of health care.

    In addition to CSAIL speakers, there were talks from members across MIT’s Institute for Data, Systems, and Society; the MIT Mobility Initiative; the MIT Media Lab; and the SENSEable City Lab.

    The proceedings concluded on that hopeful note. Rus and Werner then thanked everyone for coming. “Please continue to reflect about the good and bad of computing,” Rus urged. “And we look forward to seeing you back here in May for the next TEDxMIT event.”

    The exact theme of the spring 2022 gathering will have something to do with “superpowers.” But — if December’s mind-bending presentations were any indication — the May offering is almost certain to give its attendees plenty to think about. And maybe provide the inspiration for a startup or two. More

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    Enabling AI-driven health advances without sacrificing patient privacy

    There’s a lot of excitement at the intersection of artificial intelligence and health care. AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more.

    By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients — AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it’s difficult to verify that researchers are only using the data they need and deleting it after they’re done.

    Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner’s system. Health care organizations can control how their datasets are used, while researchers can protect the confidentiality of their models and search queries. Neither party needs to see the data or the model to collaborate.

    SAIL’s platform can also combine data from multiple sources, creating rich insights that fuel more effective algorithms.

    “You shouldn’t have to schmooze with hospital executives for five years before you can run your machine learning algorithm,” says SAIL co-founder and MIT Professor Manolis Kellis, who co-founded the company with CEO Anne Kim ’16, SM ’17. “Our goal is to help patients, to help machine learning scientists, and to create new therapeutics. We want new algorithms — the best algorithms — to be applied to the biggest possible data set.”

    SAIL has already partnered with hospitals and life science companies to unlock anonymized data for researchers. In the next year, the company hopes to be working with about half of the top 50 academic medical centers in the country.

    Unleashing AI’s full potential

    As an undergraduate at MIT studying computer science and molecular biology, Kim worked with researchers in the Computer Science and Artificial Intelligence Laboratory (CSAIL) to analyze data from clinical trials, gene association studies, hospital intensive care units, and more.

    “I realized there is something severely broken in data sharing, whether it was hospitals using hard drives, ancient file transfer protocol, or even sending stuff in the mail,” Kim says. “It was all just not well-tracked.”

    Kellis, who is also a member of the Broad Institute of MIT and Harvard, has spent years establishing partnerships with hospitals and consortia across a range of diseases including cancers, heart disease, schizophrenia, and obesity. He knew that smaller research teams would struggle to get access to the same data his lab was working with.

    In 2017, Kellis and Kim decided to commercialize technology they were developing to allow AI algorithms to run on encrypted data.

    In the summer of 2018, Kim participated in the delta v startup accelerator run by the Martin Trust Center for MIT Entrepreneurship. The founders also received support from the Sandbox Innovation Fund and the Venture Mentoring Service, and made various early connections through their MIT network.

    To participate in SAIL’s program, hospitals and other health care organizations make parts of their data available to researchers by setting up a node behind their firewall. SAIL then sends encrypted algorithms to the servers where the datasets reside in a process called federated learning. The algorithms crunch the data locally in each server and transmit the results back to a central model, which updates itself. No one — not the researchers, the data owners, or even SAIL —has access to the models or the datasets.

    The approach allows a much broader set of researchers to apply their models to large datasets. To further engage the research community, Kellis’ lab at MIT has begun holding competitions in which it gives access to datasets in areas like protein function and gene expression, and challenges researchers to predict results.

    “We invite machine learning researchers to come and train on last year’s data and predict this year’s data,” says Kellis. “If we see there’s a new type of algorithm that is performing best in these community-level assessments, people can adopt it locally at many different institutions and level the playing field. So, the only thing that matters is the quality of your algorithm rather than the power of your connections.”

    By enabling a large number of datasets to be anonymized into aggregate insights, SAIL’s technology also allows researchers to study rare diseases, in which small pools of relevant patient data are often spread out among many institutions. That has historically made the data difficult to apply AI models to.

    “We’re hoping that all of these datasets will eventually be open,” Kellis says. “We can cut across all the silos and enable a new era where every patient with every rare disorder across the entire world can come together in a single keystroke to analyze data.”

    Enabling the medicine of the future

    To work with large amounts of data around specific diseases, SAIL has increasingly sought to partner with patient associations and consortia of health care groups, including an international health care consulting company and the Kidney Cancer Association. The partnerships also align SAIL with patients, the group they’re most trying to help.

    Overall, the founders are happy to see SAIL solving problems they faced in their labs for researchers around the world.

    “The right place to solve this is not an academic project. The right place to solve this is in industry, where we can provide a platform not just for my lab but for any researcher,” Kellis says. “It’s about creating an ecosystem of academia, researchers, pharma, biotech, and hospital partners. I think it’s the blending all of these different areas that will make that vision of medicine of the future become a reality.” More

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    End-to-end supply chain transparency

    For years, companies have managed their extended supply chains with intermittent audits and certifications while attempting to persuade their suppliers to adhere to certain standards and codes of conduct. But they’ve lacked the concrete data necessary to prove their supply chains were working as they should. They most likely had baseline data about their suppliers — what they bought and who they bought it from — but knew little else about the rest of the supply chain.

    With Sourcemap, companies can now trace their supply chains from raw material to finished good with certainty, keeping track of the mines and farms that produce the commodities they rely on to take their goods to market. This unprecedented level of transparency provides Sourcemap’s customers with the assurance that the entire end-to-end supply chain operates within their standards while living up to social and environmental targets.

    And they’re doing it at scale for large multinationals across the food, agricultural, automotive, tech, and apparel industries. Thanks to Sourcemap founder and CEO Leonardo Bonanni MA ’03, SM ’05, PhD ’10, companies like VF Corporation, owner of brands like Timberland, The North Face, Mars, Hershey, and Ferrero, now have enough data to confidently tell the story of how they’re sourcing their raw materials.

    “Coming from the Media Lab, we recognized early on the power of the cloud, the power of social networking-type databases and smartphone diffusion around the world,” says Bonanni of his company’s MIT roots. Rather than providing intermittent glances at the supply chain via an auditor, Sourcemap collects data continuously, in real-time, every step of the way, flagging anything that could indicate counterfeiting, adulteration, fraud, waste, or abuse.

    “We’ve taken our customers from a situation where they had very little control to a world where they have direct visibility over their entire global operations, even allowing them to see ahead of time — before a container reaches the port — whether there is any indication that there might be something wrong with it,” says Bonanni.

    The key problem Sourcemap addresses is a lack of data in companies’ supply chain management databases. According to Bonanni, most Sourcemap customers have invested millions of dollars in enterprise resource planning (ERP) databases, which provide information about internal operations and direct suppliers, but fall short when it comes to global operations, where their secondary and tertiary suppliers operate. Built on relational databases, ERP systems have been around for more than 40 years and work well for simple, static data structures. But they aren’t agile enough to handle big data and rapidly evolving, complex data structures

    Sourcemap, on the other hand, uses NoSQL (non-relational) database technology, which is more flexible, cost-efficient, and scalable. “Our platform is like a LinkedIn for the supply chain,” explains Bonanni. Customers provide information about where they buy their raw materials, the suppliers get invited to the network and provide information to validate those relationships, right down to the farms and the mines where the raw materials are extracted — which is often where the biggest risks lie.

    Initially, the entire supply chain database of a Sourcemap customer might amount to a few megabytes of spreadsheets listing their purchase orders and the names of their suppliers. Sourcemap delivers terabytes of data that paint a detailed picture of the supply chain, capturing everything, right down to the moment a farmer in West Africa delivers cocoa beans to a warehouse, onto a truck heading to a port, to a factory, all the way to the finished goods.

    “We’ve seen the amount of data collected grow by a factor of 1 million, which tells us that the world is finally ready for full visibility of supply chains,” says Bonanni. “The fact is that we’ve seen supply chain transparency go from a fringe concern to a broad-based requirement as a license to operate in most of Europe and North America,” says Bonanni.

    These days, disruptions in supply chains, combined with price volatility and new laws requiring companies to prove that the goods they import were not made illegally (such as by causing deforestation or involving forced or child labor), means that companies are often required to know where they source their raw materials from, even if they only import the materials through an intermediary.

    Sourcemap uses its full suite of tools to walk customers through a step-by-step process that maps their suppliers while measuring performance, ultimately verifying the entire supply chain and providing them with the confidence to import goods while being customs-compliant. At the end of the day, Sourcemap customers can communicate to their stakeholders and the end consumer exactly where their commodities come from while ensuring that social, environmental, and compliance standards are met.

    The company was recently named to the newest cohort of firms honored by the MIT Startup Exchange (STEX) as STEX25 startups. Bonanni is quick to point out the benefits of STEX and of MIT’s Industrial Liaison Program (ILP): “Our best feedback and our most constructive relationships have been with companies that sponsored our research early on at the Media Lab and ILP,” he says. “The innovative exchange of ideas inherent in the MIT startup ecosystem has helped to build up Sourcemap as a company and to grow supply chain transparency as a future-facing technology that more and more companies are now scrambling to adopt.” More