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      Differences in T cells’ functional states determine resistance to cancer therapy

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      Non-small cell lung cancer (NSCLC) is the most common type of lung cancer in humans. Some patients with NSCLC receive a therapy called immune checkpoint blockade (ICB) that helps kill cancer cells by reinvigorating a subset of immune cells called T cells, which are “exhausted” and have stopped working. However, only about 35 percent of NSCLC patients respond to ICB therapy. Stefani Spranger’s lab at the MIT Department of Biology explores the mechanisms behind this resistance, with the goal of inspiring new therapies to better treat NSCLC patients. In a new study published on Oct. 29 in Science Immunology, a team led by Spranger lab postdoc Brendan Horton revealed what causes T cells to be non-responsive to ICB — and suggests a possible solution.

      Scientists have long thought that the conditions within a tumor were responsible for determining when T cells stop working and become exhausted after being overstimulated or working for too long to fight a tumor. That’s why physicians prescribe ICB to treat cancer — ICB can invigorate the exhausted T cells within a tumor. However, Horton’s new experiments show that some ICB-resistant T cells stop working before they even enter the tumor. These T cells are not actually exhausted, but rather they become dysfunctional due to changes in gene expression that arise early during the activation of a T cell, which occurs in lymph nodes. Once activated, T cells differentiate into certain functional states, which are distinguishable by their unique gene expression patterns.

      The notion that the dysfunctional state that leads to ICB resistance arises before T cells enter the tumor is quite novel, says Spranger, the Howard S. and Linda B. Stern Career Development Professor, a member of the Koch Institute for Integrative Cancer Research, and the study’s senior author.

      “We show that this state is actually a preset condition, and that the T cells are already non-responsive to therapy before they enter the tumor,” she says. As a result, she explains, ICB therapies that work by reinvigorating exhausted T cells within the tumor are less likely to be effective. This suggests that combining ICB with other forms of immunotherapy that target T cells differently might be a more effective approach to help the immune system combat this subset of lung cancer.

      In order to determine why some tumors are resistant to ICB, Horton and the research team studied T cells in murine models of NSCLC. The researchers sequenced messenger RNA from the responsive and non-responsive T cells in order to identify any differences between the T cells. Supported in part by the Koch Institute Frontier Research Program, they used a technique called Seq-Well, developed in the lab of fellow Koch Institute member J. Christopher Love, the Raymond A. (1921) and Helen E. St. Laurent Professor of Chemical Engineering and a co-author of the study. The technique allows for the rapid gene expression profiling of single cells, which permitted Spranger and Horton to get a very granular look at the gene expression patterns of the T cells they were studying.

      Seq-Well revealed distinct patterns of gene expression between the responsive and non-responsive T cells. These differences, which are determined when the T cells assume their specialized functional states, may be the underlying cause of ICB resistance.

      Now that Horton and his colleagues had a possible explanation for why some T cells did not respond to ICB, they decided to see if they could help the ICB-resistant T cells kill the tumor cells. When analyzing the gene expression patterns of the non-responsive T cells, the researchers had noticed that these T cells had a lower expression of receptors for certain cytokines, small proteins that control immune system activity. To counteract this, the researchers treated lung tumors in murine models with extra cytokines. As a result, the previously non-responsive T cells were then able to fight the tumors — meaning that the cytokine therapy prevented, and potentially even reversed, the dysfunctionality.

      Administering cytokine therapy to human patients is not currently safe, because cytokines can cause serious side effects as well as a reaction called a “cytokine storm,” which can produce severe fevers, inflammation, fatigue, and nausea. However, there are ongoing efforts to figure out how to safely administer cytokines to specific tumors. In the future, Spranger and Horton suspect that cytokine therapy could be used in combination with ICB.

      “This is potentially something that could be translated into a therapeutic that could increase the therapy response rate in non-small cell lung cancer,” Horton says.

      Spranger agrees that this work will help researchers develop more innovative cancer therapies, especially because researchers have historically focused on T cell exhaustion rather than the earlier role that T cell functional states might play in cancer.

      “If T cells are rendered dysfunctional early on, ICB is not going to be effective, and we need to think outside the box,” she says. “There’s more evidence, and other labs are now showing this as well, that the functional state of the T cell actually matters quite substantially in cancer therapies.” To Spranger, this means that cytokine therapy “might be a therapeutic avenue” for NSCLC patients beyond ICB.

      Jeffrey Bluestone, the A.W. and Mary Margaret Clausen Distinguished Professor of Metabolism and Endocrinology at the University of California-San Francisco, who was not involved with the paper, agrees. “The study provides a potential opportunity to ‘rescue’ immunity in the NSCLC non-responder patients with appropriate combination therapies,” he says.

      This research was funded by the Pew-Stewart Scholars for Cancer Research, the Ludwig Center for Molecular Oncology, the Koch Institute Frontier Research Program through the Kathy and Curt Mable Cancer Research Fund, and the National Cancer Institute. More

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      Taming the data deluge

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      An oncoming tsunami of data threatens to overwhelm huge data-rich research projects on such areas that range from the tiny neutrino to an exploding supernova, as well as the mysteries deep within the brain. 

      When LIGO picks up a gravitational-wave signal from a distant collision of black holes and neutron stars, a clock starts ticking for capturing the earliest possible light that may accompany them: time is of the essence in this race. Data collected from electrical sensors monitoring brain activity are outpacing computing capacity. Information from the Large Hadron Collider (LHC)’s smashed particle beams will soon exceed 1 petabit per second. 

      To tackle this approaching data bottleneck in real-time, a team of researchers from nine institutions led by the University of Washington, including MIT, has received $15 million in funding to establish the Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute. From MIT, the research team includes Philip Harris, assistant professor of physics, who will serve as the deputy director of the A3D3 Institute; Song Han, assistant professor of electrical engineering and computer science, who will serve as the A3D3’s co-PI; and Erik Katsavounidis, senior research scientist with the MIT Kavli Institute for Astrophysics and Space Research.

      Infused with this five-year Harnessing the Data Revolution Big Idea grant, and jointly funded by the Office of Advanced Cyberinfrastructure, A3D3 will focus on three data-rich fields: multi-messenger astrophysics, high-energy particle physics, and brain imaging neuroscience. By enriching AI algorithms with new processors, A3D3 seeks to speed up AI algorithms for solving fundamental problems in collider physics, neutrino physics, astronomy, gravitational-wave physics, computer science, and neuroscience. 

      “I am very excited about the new Institute’s opportunities for research in nuclear and particle physics,” says Laboratory for Nuclear Science Director Boleslaw Wyslouch. “Modern particle detectors produce an enormous amount of data, and we are looking for extraordinarily rare signatures. The application of extremely fast processors to sift through these mountains of data will make a huge difference in what we will measure and discover.”

      The seeds of A3D3 were planted in 2017, when Harris and his colleagues at Fermilab and CERN decided to integrate real-time AI algorithms to process the incredible rates of data at the LHC. Through email correspondence with Han, Harris’ team built a compiler, HLS4ML, that could run an AI algorithm in nanoseconds.

      “Before the development of HLS4ML, the fastest processing that we knew of was roughly a millisecond per AI inference, maybe a little faster,” says Harris. “We realized all the AI algorithms were designed to solve much slower problems, such as image and voice recognition. To get to nanosecond inference timescales, we recognized we could make smaller algorithms and rely on custom implementations with Field Programmable Gate Array (FPGA) processors in an approach that was largely different from what others were doing.”

      A few months later, Harris presented their research at a physics faculty meeting, where Katsavounidis became intrigued. Over coffee in Building 7, they discussed combining Harris’ FPGA with Katsavounidis’s use of machine learning for finding gravitational waves. FPGAs and other new processor types, such as graphics processing units (GPUs), accelerate AI algorithms to more quickly analyze huge amounts of data.

      “I had worked with the first FPGAs that were out in the market in the early ’90s and have witnessed first-hand how they revolutionized front-end electronics and data acquisition in big high-energy physics experiments I was working on back then,” recalls Katsavounidis. “The ability to have them crunch gravitational-wave data has been in the back of my mind since joining LIGO over 20 years ago.”

      Two years ago they received their first grant, and the University of Washington’s Shih-Chieh Hsu joined in. The team initiated the Fast Machine Lab, published about 40 papers on the subject, built the group to about 50 researchers, and “launched a whole industry of how to explore a region of AI that has not been explored in the past,” says Harris. “We basically started this without any funding. We’ve been getting small grants for various projects over the years. A3D3 represents our first large grant to support this effort.”  

      “What makes A3D3 so special and suited to MIT is its exploration of a technical frontier, where AI is implemented not in high-level software, but rather in lower-level firmware, reconfiguring individual gates to address the scientific question at hand,” says Rob Simcoe, director of MIT Kavli Institute for Astrophysics and Space Research and the Francis Friedman Professor of Physics. “We are in an era where experiments generate torrents of data. The acceleration gained from tailoring reprogrammable, bespoke computers at the processor level can advance real-time analysis of these data to new levels of speed and sophistication.”

      The Huge Data from the Large Hadron Collider 

      With data rates already exceeding 500 terabits per second, the LHC processes more data than any other scientific instrument on earth. Its future aggregate data rates will soon exceed 1 petabit per second, the biggest data rate in the world. 

      “Through the use of AI, A3D3 aims to perform advanced analyses, such as anomaly detection, and particle reconstruction on all collisions happening 40 million times per second,” says Harris.

      The goal is to find within all of this data a way to identify the few collisions out of the 3.2 billion collisions per second that could reveal new forces, explain how dark matter is formed, and complete the picture of how fundamental forces interact with matter. Processing all of this information requires a customized computing system capable of interpreting the collider information within ultra-low latencies.  

      “The challenge of running this on all of the 100s of terabits per second in real-time is daunting and requires a complete overhaul of how we design and implement AI algorithms,” says Harris. “With large increases in the detector resolution leading to data rates that are even larger the challenge of finding the one collision, among many, will become even more daunting.” 

      The Brain and the Universe

      Thanks to advances in techniques such as medical imaging and electrical recordings from implanted electrodes, neuroscience is also gathering larger amounts of data on how the brain’s neural networks process responses to stimuli and perform motor information. A3D3 plans to develop and implement high-throughput and low-latency AI algorithms to process, organize, and analyze massive neural datasets in real time, to probe brain function in order to enable new experiments and therapies.   

      With Multi-Messenger Astrophysics (MMA), A3D3 aims to quickly identify astronomical events by efficiently processing data from gravitational waves, gamma-ray bursts, and neutrinos picked up by telescopes and detectors. 

      The A3D3 researchers also include a multi-disciplinary group of 15 other researchers, including project lead the University of Washington, along with Caltech, Duke University, Purdue University, UC San Diego, University of Illinois Urbana-Champaign, University of Minnesota, and the University of Wisconsin-Madison. It will include neutrinos research at Icecube and DUNE, and visible astronomy at Zwicky Transient Facility, and will organize deep-learning workshops and boot camps to train students and researchers on how to contribute to the framework and widen the use of fast AI strategies.

      “We have reached a point where detector network growth will be transformative, both in terms of event rates and in terms of astrophysical reach and ultimately, discoveries,” says Katsavounidis. “‘Fast’ and ‘efficient’ is the only way to fight the ‘faint’ and ‘fuzzy’ that is out there in the universe, and the path for getting the most out of our detectors. A3D3 on one hand is going to bring production-scale AI to gravitational-wave physics and multi-messenger astronomy; but on the other hand, we aspire to go beyond our immediate domains and become the go-to place across the country for applications of accelerated AI to data-driven disciplines.” More

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      New integrative computational neuroscience center established at MIT’s McGovern Institute

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      With the tools of modern neuroscience, researchers can peer into the brain with unprecedented accuracy. Recording devices listen in on the electrical conversations between neurons, picking up the voices of hundreds of cells at a time. Genetic tools allow us to focus on specific types of neurons based on their molecular signatures. Microscopes zoom in to illuminate the brain’s circuitry, capturing thousands of images of elaborately branched dendrites. Functional MRIs detect changes in blood flow to map activity within a person’s brain, generating a complete picture by compiling hundreds of scans.

      This deluge of data provides insights into brain function and dynamics at different levels — molecules, cells, circuits, and behavior — but the insights remain compartmentalized in separate research silos for each level. An innovative new center at MIT’s McGovern Institute for Brain Research aims to leverage them into powerful revelations of the brain’s inner workings.

      The K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center will create advanced mathematical models and computational tools to synthesize the deluge of data across scales and advance our understanding of the brain and mental health.

      The center, funded by a $24 million donation from philanthropist Lisa Yang and led by McGovern Institute Associate Investigator Ila Fiete, will take a collaborative approach to computational neuroscience, integrating cutting-edge modeling techniques and data from MIT labs to explain brain function at every level, from the molecular to the behavioral.

      “Our goal is that sophisticated, truly integrated computational models of the brain will make it possible to identify how ‘control knobs’ such as genes, proteins, chemicals, and environment drive thoughts and behavior, and to make inroads toward urgent unmet needs in understanding and treating brain disorders,” says Fiete, who is also a brain and cognitive sciences professor at MIT.

      “Driven by technologies that generate massive amounts of data, we are entering a new era of translational neuroscience research,” says Yang, whose philanthropic investment in MIT research now exceeds $130 million. “I am confident that the multidisciplinary expertise convened by the ICoN center will revolutionize how we synthesize this data and ultimately understand the brain in health and disease.”

      Connecting the data

      It is impossible to separate the molecules in the brain from their effects on behavior — although those aspects of neuroscience have traditionally been studied independently, by researchers with vastly different expertise. The ICoN Center will eliminate the divides, bringing together neuroscientists and software engineers to deal with all types of data about the brain.

      “The center’s highly collaborative structure, which is essential for unifying multiple levels of understanding, will enable us to recruit talented young scientists eager to revolutionize the field of computational neuroscience,” says Robert Desimone, director of the McGovern Institute. “It is our hope that the ICoN Center’s unique research environment will truly demonstrate a new academic research structure that catalyzes bold, creative research.”

      To foster interdisciplinary collaboration, every postdoc and engineer at the center will work with multiple faculty mentors. In order to attract young scientists and engineers to the field of computational neuroscience, the center will also provide four graduate fellowships to MIT students each year in perpetuity. Interacting closely with three scientific cores, engineers and fellows will develop computational models and technologies for analyzing molecular data, neural circuits, and behavior, such as tools to identify patterns in neural recordings or automate the analysis of human behavior to aid psychiatric diagnoses. These technologies and models will be instrumental in synthesizing data into knowledge and understanding.

      Center priorities

      In its first five years, the ICoN Center will prioritize four areas of investigation: episodic memory and exploration, including functions like navigation and spatial memory; complex or stereotypical behavior, such as the perseverative behaviors associated with autism and obsessive-compulsive disorder; cognition and attention; and sleep. Models of complex behavior will be created in collaboration with clinicians and researchers at Children’s Hospital of Philadelphia.

      The goal, Fiete says, is to model the neuronal interactions that underlie these functions so that researchers can predict what will happen when something changes — when certain neurons become more active or when a genetic mutation is introduced, for example. When paired with experimental data from MIT labs, the center’s models will help explain not just how these circuits work, but also how they are altered by genes, the environment, aging, and disease. These focus areas encompass circuits and behaviors often affected by psychiatric disorders and neurodegeneration, and models will give researchers new opportunities to explore their origins and potential treatment strategies.

      “Lisa Yang is focused on helping the scientific community realize its goals in translational research,” says Nergis Mavalvala, dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “With her generous support, we can accelerate the pace of research by connecting the data to the delivery of tangible results.” More

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      Study: Global cancer risk from burning organic matter comes from unregulated chemicals

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      Whenever organic matter is burned, such as in a wildfire, a power plant, a car’s exhaust, or in daily cooking, the combustion releases polycyclic aromatic hydrocarbons (PAHs) — a class of pollutants that is known to cause lung cancer.

      There are more than 100 known types of PAH compounds emitted daily into the atmosphere. Regulators, however, have historically relied on measurements of a single compound, benzo(a)pyrene, to gauge a community’s risk of developing cancer from PAH exposure. Now MIT scientists have found that benzo(a)pyrene may be a poor indicator of this type of cancer risk.

      In a modeling study appearing today in the journal GeoHealth, the team reports that benzo(a)pyrene plays a small part — about 11 percent — in the global risk of developing PAH-associated cancer. Instead, 89 percent of that cancer risk comes from other PAH compounds, many of which are not directly regulated.

      Interestingly, about 17 percent of PAH-associated cancer risk comes from “degradation products” — chemicals that are formed when emitted PAHs react in the atmosphere. Many of these degradation products can in fact be more toxic than the emitted PAH from which they formed.

      The team hopes the results will encourage scientists and regulators to look beyond benzo(a)pyrene, to consider a broader class of PAHs when assessing a community’s cancer risk.

      “Most of the regulatory science and standards for PAHs are based on benzo(a)pyrene levels. But that is a big blind spot that could lead you down a very wrong path in terms of assessing whether cancer risk is improving or not, and whether it’s relatively worse in one place than another,” says study author Noelle Selin, a professor in MIT’s Institute for Data, Systems and Society, and the Department of Earth, Atmospheric and Planetary Sciences.

      Selin’s MIT co-authors include Jesse Kroll, Amy Hrdina, Ishwar Kohale, Forest White, and Bevin Engelward, and Jamie Kelly (who is now at University College London). Peter Ivatt and Mathew Evans at the University of York are also co-authors.

      Chemical pixels

      Benzo(a)pyrene has historically been the poster chemical for PAH exposure. The compound’s indicator status is largely based on early toxicology studies. But recent research suggests the chemical may not be the PAH representative that regulators have long relied upon.   

      “There has been a bit of evidence suggesting benzo(a)pyrene may not be very important, but this was from just a few field studies,” says Kelly, a former postdoc in Selin’s group and the study’s lead author.

      Kelly and his colleagues instead took a systematic approach to evaluate benzo(a)pyrene’s suitability as a PAH indicator. The team began by using GEOS-Chem, a global, three-dimensional chemical transport model that breaks the world into individual grid boxes and simulates within each box the reactions and concentrations of chemicals in the atmosphere.

      They extended this model to include chemical descriptions of how various PAH compounds, including benzo(a)pyrene, would react in the atmosphere. The team then plugged in recent data from emissions inventories and meteorological observations, and ran the model forward to simulate the concentrations of various PAH chemicals around the world over time.

      Risky reactions

      In their simulations, the researchers started with 16 relatively well-studied PAH chemicals, including benzo(a)pyrene, and traced the concentrations of these chemicals, plus the concentration of their degradation products over two generations, or chemical transformations. In total, the team evaluated 48 PAH species.

      They then compared these concentrations with actual concentrations of the same chemicals, recorded by monitoring stations around the world. This comparison was close enough to show that the model’s concentration predictions were realistic.

      Then within each model’s grid box, the researchers related the concentration of each PAH chemical to its associated cancer risk; to do this, they had to develop a new method based on previous studies in the literature to avoid double-counting risk from the different chemicals. Finally, they overlaid population density maps to predict the number of cancer cases globally, based on the concentration and toxicity of a specific PAH chemical in each location.

      Dividing the cancer cases by population produced the cancer risk associated with that chemical. In this way, the team calculated the cancer risk for each of the 48 compounds, then determined each chemical’s individual contribution to the total risk.

      This analysis revealed that benzo(a)pyrene had a surprisingly small contribution, of about 11 percent, to the overall risk of developing cancer from PAH exposure globally. Eighty-nine percent of cancer risk came from other chemicals. And 17 percent of this risk arose from degradation products.

      “We see places where you can find concentrations of benzo(a)pyrene are lower, but the risk is higher because of these degradation products,” Selin says. “These products can be orders of magnitude more toxic, so the fact that they’re at tiny concentrations doesn’t mean you can write them off.”

      When the researchers compared calculated PAH-associated cancer risks around the world, they found significant differences depending on whether that risk calculation was based solely on concentrations of benzo(a)pyrene or on a region’s broader mix of PAH compounds.

      “If you use the old method, you would find the lifetime cancer risk is 3.5 times higher in Hong Kong versus southern India, but taking into account the differences in PAH mixtures, you get a difference of 12 times,” Kelly says. “So, there’s a big difference in the relative cancer risk between the two places. And we think it’s important to expand the group of compounds that regulators are thinking about, beyond just a single chemical.”

      The team’s study “provides an excellent contribution to better understanding these ubiquitous pollutants,” says Elisabeth Galarneau, an air quality expert and PhD research scientist in Canada’s Department of the Environment. “It will be interesting to see how these results compare to work being done elsewhere … to pin down which (compounds) need to be tracked and considered for the protection of human and environmental health.”

      This research was conducted in MIT’s Superfund Research Center and is supported in part by the National Institute of Environmental Health Sciences Superfund Basic Research Program, and the National Institutes of Health. More

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      MIT appoints members of new faculty committee to drive climate action plan

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      In May, responding to the world’s accelerating climate crisis, MIT issued an ambitious new plan, “Fast Forward: MIT’s Climate Action Plan for the Decade.” The plan outlines a broad array of new and expanded initiatives across campus to build on the Institute’s longstanding climate work.

      Now, to unite these varied climate efforts, maximize their impact, and identify new ways for MIT to contribute climate solutions, the Institute has appointed more than a dozen faculty members to a new committee established by the Fast Forward plan, named the Climate Nucleus.

      The committee includes leaders of a number of climate- and energy-focused departments, labs, and centers that have significant responsibilities under the plan. Its membership spans all five schools and the MIT Schwarzman College of Computing. Professors Noelle Selin and Anne White have agreed to co-chair the Climate Nucleus for a term of three years.

      “I am thrilled and grateful that Noelle and Anne have agreed to step up to this important task,” says Maria T. Zuber, MIT’s vice president for research. “Under their leadership, I’m confident that the Climate Nucleus will bring new ideas and new energy to making the strategy laid out in the climate action plan a reality.”

      The Climate Nucleus has broad responsibility for the management and implementation of the Fast Forward plan across its five areas of action: sparking innovation, educating future generations, informing and leveraging government action, reducing MIT’s own climate impact, and uniting and coordinating all of MIT’s climate efforts.

      Over the next few years, the nucleus will aim to advance MIT’s contribution to a two-track approach to decarbonizing the global economy, an approach described in the Fast Forward plan. First, humanity must go as far and as fast as it can to reduce greenhouse gas emissions using existing tools and methods. Second, societies need to invest in, invent, and deploy new tools — and promote new institutions and policies — to get the global economy to net-zero emissions by mid-century.

      The co-chairs of the nucleus bring significant climate and energy expertise, along with deep knowledge of the MIT community, to their task.

      Selin is a professor with joint appointments in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences. She is also the director of the Technology and Policy Program. She began at MIT in 2007 as a postdoc with the Center for Global Change Science and the Joint Program on the Science and Policy of Global Change. Her research uses modeling to inform decision-making on air pollution, climate change, and hazardous substances.

      “Climate change affects everything we do at MIT. For the new climate action plan to be effective, the Climate Nucleus will need to engage the entire MIT community and beyond, including policymakers as well as people and communities most affected by climate change,” says Selin. “I look forward to helping to guide this effort.”

      White is the School of Engineering’s Distinguished Professor of Engineering and the head of the Department of Nuclear Science and Engineering. She joined the MIT faculty in 2009 and has also served as the associate director of MIT’s Plasma Science and Fusion Center. Her research focuses on assessing and refining the mathematical models used in the design of fusion energy devices, such as tokamaks, which hold promise for delivering limitless zero-carbon energy.

      “The latest IPCC report underscores the fact that we have no time to lose in decarbonizing the global economy quickly. This is a problem that demands we use every tool in our toolbox — and develop new ones — and we’re committed to doing that,” says White, referring to an August 2021 report from the Intergovernmental Panel on Climate Change, a UN climate science body, that found that climate change has already affected every region on Earth and is intensifying. “We must train future technical and policy leaders, expand opportunities for students to work on climate problems, and weave sustainability into every one of MIT’s activities. I am honored to be a part of helping foster this Institute-wide collaboration.”

      A first order of business for the Climate Nucleus will be standing up three working groups to address specific aspects of climate action at MIT: climate education, climate policy, and MIT’s own carbon footprint. The working groups will be responsible for making progress on their particular areas of focus under the plan and will make recommendations to the nucleus on ways of increasing MIT’s effectiveness and impact. The working groups will also include student, staff, and alumni members, so that the entire MIT community has the opportunity to contribute to the plan’s implementation.  

      The nucleus, in turn, will report and make regular recommendations to the Climate Steering Committee, a senior-level team consisting of Zuber; Richard Lester, the associate provost for international activities; Glen Shor, the executive vice president and treasurer; and the deans of the five schools and the MIT Schwarzman College of Computing. The new plan created the Climate Steering Committee to ensure that climate efforts will receive both the high-level attention and the resources needed to succeed.

      Together the new committees and working groups are meant to form a robust new infrastructure for uniting and coordinating MIT’s climate action efforts in order to maximize their impact. They replace the Climate Action Advisory Committee, which was created in 2016 following the release of MIT’s first climate action plan.

      In addition to Selin and White, the members of the Climate Nucleus are:

      Bob Armstrong, professor in the Department of Chemical Engineering and director of the MIT Energy Initiative;
      Dara Entekhabi, professor in the departments of Civil and Environmental Engineering and Earth, Atmospheric and Planetary Sciences;
      John Fernández, professor in the Department of Architecture and director of the Environmental Solutions Initiative;
      Stefan Helmreich, professor in the Department of Anthropology;
      Christopher Knittel, professor in the MIT Sloan School of Management and director of the Center for Energy and Environmental Policy Research;
      John Lienhard, professor in the Department of Mechanical Engineering and director of the Abdul Latif Jameel Water and Food Systems Lab;
      Julie Newman, director of the Office of Sustainability and lecturer in the Department of Urban Studies and Planning;
      Elsa Olivetti, professor in the Department of Materials Science and Engineering and co-director of the Climate and Sustainability Consortium;
      Christoph Reinhart, professor in the Department of Architecture and director of the Building Technology Program;
      John Sterman, professor in the MIT Sloan School of Management and director of the Sloan Sustainability Initiative;
      Rob van der Hilst, professor and head of the Department of Earth, Atmospheric and Planetary Sciences; and
      Chris Zegras, professor and head of the Department of Urban Studies and Planning. More

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      MIT welcomes nine MLK Visiting Professors and Scholars for 2021-22

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      In its 31st year, the Martin Luther King Jr. (MLK) Visiting Professors and Scholars Program will host nine outstanding scholars from across the Americas. The flagship program honors the life and legacy of Martin Luther King Jr. by increasing the presence and recognizing the contributions of underrepresented minority scholars at MIT. Throughout the year, the cohort will enhance their scholarship through intellectual engagement with the MIT community and enrich the cultural, academic, and professional experience of students.

      The 2021-22 scholars

      Sanford Biggers is an interdisciplinary artist hosted by the Department of Architecture. His work is an interplay of narrative, perspective, and history that speaks to current social, political, and economic happenings while examining their contexts. His diverse practice positions him as a collaborator with the past through explorations of often-overlooked cultural and political narratives from American history. Through collaboration with his faculty host, Brandon Clifford, he will spend the year contributing to projects with Architecture; Art, Culture and Technology; the Transmedia Storytelling initiatives; and community workshops and engagement with local K-12 education.

      Kristen Dorsey is an assistant professor of engineering at Smith College. She will be hosted by the Program in Media Arts and Sciences at the MIT Media Lab. Her research focuses on the fabrication and characterization of microscale sensors and microelectromechanical systems. Dorsey tries to understand “why things go wrong” by investigating device reliability and stability. At MIT, Dorsey is interested in forging collaborations to consider issues of access and equity as they apply to wearable health care devices.

      Omolola “Lola” Eniola-Adefeso is the associate dean for graduate and professional education and associate professor of chemical engineering at the University of Michigan. She will join MIT’s Department of Chemical Engineering (ChemE). Eniola-Adefeso will work with Professor Paula Hammond on developing electrostatically assembled nanoparticle coatings that enable targeting of specific immune cell types. A co-founder and chief scientific officer of Asalyxa Bio, she is interested in the interactions between blood leukocytes and endothelial cells in vessel lumen lining, and how they change during inflammation response. Eniola-Adefeso will also work with the Diversity in Chemical Engineering (DICE) graduate student group in ChemE and the National Organization of Black Chemists and Chemical Engineers.

      Robert Gilliard Jr. is an assistant professor of chemistry at the University of Virginia and will join the MIT chemistry department, working closely with faculty host Christopher Cummins. His research focuses on various aspects of group 15 element chemistry. He was a founding member of the National Organization of Black Chemists and Chemical Engineers UGA section, and he has served as an American Chemical Society (ACS) Bridge Program mentor as well as an ACS Project Seed mentor. Gilliard has also collaborated with the Cleveland Public Library to expose diverse young scholars to STEM fields.

      Valencia Joyner Koomson ’98, MNG ’99 will return for the second semester of her appointment this fall in MIT’s Department of Electrical Engineering and Computer Science. Based at Tufts University, where she is an associate professor in the Department of Electrical and Computer Engineering, Koomson has focused her research on microelectronic systems for cell analysis and biomedical applications. In the past semester, she has served as a judge for the Black Alumni/ae of MIT Research Slam and worked closely with faculty host Professor Akintunde Akinwande.

      Luis Gilberto Murillo-Urrutia will continue his appointment in MIT’s Environmental Solutions Initiative. He has 30 years of experience in public policy design, implementation, and advocacy, most notably in the areas of sustainable regional development, environmental protection and management of natural resources, social inclusion, and peace building. At MIT, he has continued his research on environmental justice, with a focus on carbon policy and its impacts on Afro-descendant communities in Colombia.

      Sonya T. Smith was the first female professor of mechanical engineering at Howard University. She will join the Department of Aeronautics and Astronautics at MIT. Her research involves computational fluid dynamics and thermal management of electronics for air and space vehicles. She is looking forward to serving as a mentor to underrepresented students across MIT and fostering new research collaborations with her home lab at Howard.

      Lawrence Udeigwe is an associate professor of mathematics at Manhattan College and will join MIT’s Department of Brain and Cognitive Sciences. He plans to co-teach a graduate seminar course with Professor James DiCarlo to explore practical and philosophical questions regarding the use of simulations to build theories in neuroscience. Udeigwe also leads the Lorens Chuno group; as a singer-songwriter, his work tackles intersectionality issues faced by contemporary Africans.

      S. Craig Watkins is an internationally recognized expert in media and a professor at the University of Texas at Austin. He will join MIT’s Institute for Data, Systems, and Society to assist in researching the role of big data in enabling deep structural changes with regard to systemic racism. He will continue to expand on his work as founding director of the Institute for Media Innovation at the University of Texas at Austin, exploring the intersections of critical AI studies, critical race studies, and design. He will also work with MIT’s Center for Advanced Virtuality to develop computational systems that support social perspective-taking.

      Community engagement

      Throughout the 2021-22 academic year, MLK professors and scholars will be presenting their research at a monthly speaker series. Events will be held in an in-person/Zoom hybrid environment. All members of the MIT community are encouraged to attend and hear directly from this year’s cohort of outstanding scholars. To hear more about upcoming events, subscribe to their mailing list.

      On Sept. 15, all are invited to join the Institute Community and Equity Office in welcoming the scholars to campus by attending a welcome luncheon. More

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      in Data Management & Statistics

      Using adversarial attacks to refine molecular energy predictions

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      Neural networks (NNs) are increasingly being used to predict new materials, the rate and yield of chemical reactions, and drug-target interactions, among others. For these applications, they are orders of magnitude faster than traditional methods such as quantum mechanical simulations. 

      The price for this agility, however, is reliability. Because machine learning models only interpolate, they may fail when used outside the domain of training data.

      But the part that worried Rafael Gómez-Bombarelli, the Jeffrey Cheah Career Development Professor in the MIT Department of Materials Science and Engineering, and graduate students Daniel Schwalbe-Koda and Aik Rui Tan was that establishing the limits of these machine learning (ML) models is tedious and labor-intensive. 

      This is particularly true for predicting ‘‘potential energy surfaces” (PES), or the map of a molecule’s energy in all its configurations. These surfaces encode the complexities of a molecule into flatlands, valleys, peaks, troughs, and ravines. The most stable configurations of a system are usually in the deep pits — quantum mechanical chasms from which atoms and molecules typically do not escape. 

      In a recent Nature Communications paper, the research team presented a way to demarcate the “safe zone” of a neural network by using “adversarial attacks.” Adversarial attacks have been studied for other classes of problems, such as image classification, but this is the first time that they are being used to sample molecular geometries in a PES. 

      “People have been using uncertainty for active learning for years in ML potentials. The key difference is that they need to run the full ML simulation and evaluate if the NN was reliable, and if it wasn’t, acquire more data, retrain and re-simulate. Meaning that it takes a long time to nail down the right model, and one has to run the ML simulation many times” explains Gómez-Bombarelli.

      The Gómez-Bombarelli lab at MIT works on a synergistic synthesis of first-principles simulation and machine learning that greatly speeds up this process. The actual simulations are run only for a small fraction of these molecules, and all those data are fed into a neural network that learns how to predict the same properties for the rest of the molecules. They have successfully demonstrated these methods for a growing class of novel materials that includes catalysts for producing hydrogen from water, cheaper polymer electrolytes for electric vehicles,  zeolites for molecular sieving, magnetic materials, and more. 

      The challenge, however, is that these neural networks are only as smart as the data they are trained on.  Considering the PES map, 99 percent of the data may fall into one pit, totally missing valleys that are of more interest. 

      Such wrong predictions can have disastrous consequences — think of a self-driving car that fails to identify a person crossing the street.

      One way to find out the uncertainty of a model is to run the same data through multiple versions of it. 

      For this project, the researchers had multiple neural networks predict the potential energy surface from the same data. Where the network is fairly sure of the prediction, the variation between the outputs of different networks is minimal and the surfaces largely converge. When the network is uncertain, the predictions of different models vary widely, producing a range of outputs, any of which could be the correct surface. 

      The spread in the predictions of a “committee of neural networks” is the “uncertainty” at that point. A good model should not just indicate the best prediction, but also indicates the uncertainty about each of these predictions. It’s like the neural network says “this property for material A will have a value of X and I’m highly confident about it.”

      This could have been an elegant solution but for the sheer scale of the combinatorial space. “Each simulation (which is ground feed for the neural network) may take from tens to thousands of CPU hours,” explains Schwalbe-Koda. For the results to be meaningful, multiple models must be run over a sufficient number of points in the PES, an extremely time-consuming process. 

      Instead, the new approach only samples data points from regions of low prediction confidence, corresponding to specific geometries of a molecule. These molecules are then stretched or deformed slightly so that the uncertainty of the neural network committee is maximized. Additional data are computed for these molecules through simulations and then added to the initial training pool. 

      The neural networks are trained again, and a new set of uncertainties are calculated. This process is repeated until the uncertainty associated with various points on the surface becomes well-defined and cannot be decreased any further. 

      Gómez-Bombarelli explains, “We aspire to have a model that is perfect in the regions we care about (i.e., the ones that the simulation will visit) without having had to run the full ML simulation, by making sure that we make it very good in high-likelihood regions where it isn’t.”

      The paper presents several examples of this approach, including predicting complex supramolecular interactions in zeolites. These materials are cavernous crystals that act as molecular sieves with high shape selectivity. They find applications in catalysis, gas separation, and ion exchange, among others.

      Because performing simulations of large zeolite structures is very costly, the researchers show how their method can provide significant savings in computational simulations. They used more than 15,000 examples to train a neural network to predict the potential energy surfaces for these systems. Despite the large cost required to generate the dataset, the final results are mediocre, with only around 80 percent of the neural network-based simulations being successful. To improve the performance of the model using traditional active learning methods, the researchers calculated an additional 5,000 data points, which improved the performance of the neural network potentials to 92 percent.

      However, when the adversarial approach is used to retrain the neural networks, the authors saw a performance jump to 97 percent using only 500 extra points. That’s a remarkable result, the researchers say, especially considering that each of these extra points takes hundreds of CPU hours. 

      This could be the most realistic method to probe the limits of models that researchers use to predict the behavior of materials and the progress of chemical reactions. More

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      in Data Management & Statistics

      Helping companies optimize their websites and mobile apps

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      Creating a good customer experience increasingly means creating a good digital experience. But metrics like pageviews and clicks offer limited insight into how much customers actually like a digital product.

      That’s the problem the digital optimization company Amplitude is solving. Amplitude gives companies a clearer picture into how users interact with their digital products to help them understand exactly which features to promote or improve.

      “It’s all about using product data to drive your business,” says Amplitude CEO Spenser Skates ’10, who co-founded the company with Curtis Liu ’10 and Stanford University graduate Jeffrey Wang. “Mobile apps and websites are really complex. The average app or website will have thousands of things you can do with it. The question is how you know which of those things are driving a great user experience and which parts are really frustrating for users.”

      Amplitude’s database can gather millions of details about how users behave inside an app or website and allow customers to explore that information without needing data science degrees.

      “It provides an interface for very easy, accessible ways of looking at your data, understanding your data, and asking questions of that data,” Skates says.

      Amplitude, which recently announced it will be going public, is already helping 23 of the 100 largest companies in the U.S. Customers include media companies like NBC, tech companies like Twitter, and retail companies like Walmart.

      “Our platform helps businesses understand how people are using their apps and websites so they can create better versions of their products,” Skates says. “It’s all about creating a really compelling product.”

      Learning entrepreneurship

      The founders say their years at MIT were among the best of their lives. Skates and Liu were undergraduates from 2006 to 2010. Skates majored in biological engineering while Liu majored in mathematics and electrical engineering and computer science. The two first met as opponents in MIT’s Battlecode competition, in which students use artificial intelligence algorithms to control teams of robots that compete in a strategy game against other teams. The following year they teamed up.

      “There are a lot of parallels between what you’re trying to do in Battlecode and what you end up having to do in the early stages of a startup,” Liu says. “You have limited resources, limited time, and you’re trying to accomplish a goal. What we found is trying a lot of different things, putting our ideas out there and testing them with real data, really helped us focus on the things that actually mattered. That method of iteration and continual improvement set the foundation for how we approach building products and startups.”

      Liu and Skates next participated in the MIT $100K Entrepreneurship Competition with an idea for a cloud-based music streaming service. After graduation, Skates began working in finance and Liu got a job at Google, but they continued pursuing startup ideas on the side, including a website that let alumni see where their classmates ended up and a marketplace for finding photographers.

      A year after graduation, the founders decided to quit their jobs and work on a startup full time. Skates moved into Liu’s apartment in San Francisco, setting up a mattress on the floor, and they began working on a project that became Sonalight, a voice recognition app. As part of the project, the founders built an internal system to understand where users got stuck in the app and what features were used the most.

      Despite getting over 100,000 downloads, the founders decided Sonalight was a little too early for its time and started thinking their analytics feature could be useful to other companies. They spoke with about 30 different product teams to learn more about what companies wanted from their digital analytics. Amplitude was officially founded in 2012.

      Amplitude gathers fine details about digital product usage, parsing out individual features and actions to give customers a better view of how their products are being used. Using the data in Amplitude’s intuitive, no-code interface, customers can make strategic decisions like whether to launch a feature or change a distribution channel.

      The platform is designed to ease the bottlenecks that arise when executives, product teams, salespeople, and marketers want to answer questions about customer experience or behavior but need the data science team to crunch the numbers for them.

      “It’s a very collaborative interface to encourage customers to work together to understand how users are engaging with their apps,” Skates says.

      Amplitude’s database also uses machine learning to segment users, predict user outcomes, and uncover novel correlations. Earlier this year, the company unveiled a service called Recommend that helps companies create personalized user experiences across their entire platform in minutes. The service goes beyond demographics to personalize customer experiences based on what users have done or seen before within the product.

      “We’re very conscious on the privacy front,” Skates says. “A lot of analytics companies will resell your data to third parties or use it for advertising purposes. We don’t do any of that. We’re only here to provide product insights to our customers. We’re not using data to track you across the web. Everyone expects Netflix to use the data on what you’ve watched before to recommend what to watch next. That’s effectively what we’re helping other companies do.”

      Optimizing digital experiences

      The meditation app Calm is on a mission to help users build habits that improve their mental wellness. Using Amplitude, the company learned that users most often use the app to get better sleep and reduce stress. The insights helped Calm’s team double down on content geared toward those goals, launching “sleep stories” to help users unwind at the end of each day and adding content around anxiety relief and relaxation. Sleep stories are now Calm’s most popular type of content, and Calm has grown rapidly to millions of people around the world.

      Calm’s story shows the power of letting user behavior drive product decisions. Amplitude has also helped the online fundraising site GoFundMe increase donations by showing users more compelling campaigns and the exercise bike company Peloton realize the importance of social features like leaderboards.

      Moving forward, the founders believe Amplitude’s platform will continue helping companies adapt to an increasingly digital world in which users expect more compelling, personalized experiences.

      “If you think about the online experience for companies today compared to 10 years ago, now [digital] is the main point of contact, whether you’re a media company streaming content, a retail company, or a finance company,” Skates says. “That’s only going to continue. That’s where we’re trying to help.” More