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      Understanding air pollution from space

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      Climate change and air pollution are interlocking crises that threaten human health. Reducing emissions of some air pollutants can help achieve climate goals, and some climate mitigation efforts can in turn improve air quality.

      One part of MIT Professor Arlene Fiore’s research program is to investigate the fundamental science in understanding air pollutants — how long they persist and move through our environment to affect air quality.

      “We need to understand the conditions under which pollutants, such as ozone, form. How much ozone is formed locally and how much is transported long distances?” says Fiore, who notes that Asian air pollution can be transported across the Pacific Ocean to North America. “We need to think about processes spanning local to global dimensions.”

      Fiore, the Peter H. Stone and Paola Malanotte Stone Professor in Earth, Atmospheric and Planetary Sciences, analyzes data from on-the-ground readings and from satellites, along with models, to better understand the chemistry and behavior of air pollutants — which ultimately can inform mitigation strategies and policy setting.

      A global concern

      At the United Nations’ most recent climate change conference, COP26, air quality management was a topic discussed over two days of presentations.

      “Breathing is vital. It’s life. But for the vast majority of people on this planet right now, the air that they breathe is not giving life, but cutting it short,” said Sarah Vogel, senior vice president for health at the Environmental Defense Fund, at the COP26 session.

      “We need to confront this twin challenge now through both a climate and clean air lens, of targeting those pollutants that warm both the air and harm our health.”

      Earlier this year, the World Health Organization (WHO) updated its global air quality guidelines it had issued 15 years earlier for six key pollutants including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The new guidelines are more stringent based on what the WHO stated is the “quality and quantity of evidence” of how these pollutants affect human health. WHO estimates that roughly 7 million premature deaths are attributable to the joint effects of air pollution.

      “We’ve had all these health-motivated reductions of aerosol and ozone precursor emissions. What are the implications for the climate system, both locally but also around the globe? How does air quality respond to climate change? We study these two-way interactions between air pollution and the climate system,” says Fiore.

      But fundamental science is still required to understand how gases, such as ozone and nitrogen dioxide, linger and move throughout the troposphere — the lowermost layer of our atmosphere, containing the air we breathe.

      “We care about ozone in the air we’re breathing where we live at the Earth’s surface,” says Fiore. “Ozone reacts with biological tissue, and can be damaging to plants and human lungs. Even if you’re a healthy adult, if you’re out running hard during an ozone smog event, you might feel an extra weight on your lungs.”

      Telltale signs from space

      Ozone is not emitted directly, but instead forms through chemical reactions catalyzed by radiation from the sun interacting with nitrogen oxides — pollutants released in large part from burning fossil fuels—and volatile organic compounds. However, current satellite instruments cannot sense ground-level ozone.

      “We can’t retrieve surface- or even near-surface ozone from space,” says Fiore of the satellite data, “although the anticipated launch of a new instrument looks promising for new advances in retrieving lower-tropospheric ozone”. Instead, scientists can look at signatures from other gas emissions to get a sense of ozone formation. “Nitrogen dioxide and formaldehyde are a heavy focus of our research because they serve as proxies for two of the key ingredients that go on to form ozone in the atmosphere.”

      To understand ozone formation via these precursor pollutants, scientists have gathered data for more than two decades using spectrometer instruments aboard satellites that measure sunlight in ultraviolet and visible wavelengths that interact with these pollutants in the Earth’s atmosphere — known as solar backscatter radiation.

      Satellites, such as NASA’s Aura, carry instruments like the Ozone Monitoring Instrument (OMI). OMI, along with European-launched satellites such as the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY), and the newest generation TROPOspheric Monitoring instrument (TROPOMI), all orbit the Earth, collecting data during daylight hours when sunlight is interacting with the atmosphere over a particular location.

      In a recent paper from Fiore’s group, former graduate student Xiaomeng Jin (now a postdoc at the University of California at Berkeley), demonstrated that she could bring together and “beat down the noise in the data,” as Fiore says, to identify trends in ozone formation chemistry over several U.S. metropolitan areas that “are consistent with our on-the-ground understanding from in situ ozone measurements.”

      “This finding implies that we can use these records to learn about changes in surface ozone chemistry in places where we lack on-the-ground monitoring,” says Fiore. Extracting these signals by stringing together satellite data — OMI, GOME, and SCIAMACHY — to produce a two-decade record required reconciling the instruments’ differing orbit days, times, and fields of view on the ground, or spatial resolutions. 

      Currently, spectrometer instruments aboard satellites are retrieving data once per day. However, newer instruments, such as the Geostationary Environment Monitoring Spectrometer launched in February 2020 by the National Institute of Environmental Research in the Ministry of Environment of South Korea, will monitor a particular region continuously, providing much more data in real time.

      Over North America, the Tropospheric Emissions: Monitoring of Pollution Search (TEMPO) collaboration between NASA and the Smithsonian Astrophysical Observatory, led by Kelly Chance of Harvard University, will provide not only a stationary view of the atmospheric chemistry over the continent, but also a finer-resolution view — with the instrument recording pollution data from only a few square miles per pixel (with an anticipated launch in 2022).

      “What we’re very excited about is the opportunity to have continuous coverage where we get hourly measurements that allow us to follow pollution from morning rush hour through the course of the day and see how plumes of pollution are evolving in real time,” says Fiore.

      Data for the people

      Providing Earth-observing data to people in addition to scientists — namely environmental managers, city planners, and other government officials — is the goal for the NASA Health and Air Quality Applied Sciences Team (HAQAST).

      Since 2016, Fiore has been part of HAQAST, including collaborative “tiger teams” — projects that bring together scientists, nongovernment entities, and government officials — to bring data to bear on real issues.

      For example, in 2017, Fiore led a tiger team that provided guidance to state air management agencies on how satellite data can be incorporated into state implementation plans (SIPs). “Submission of a SIP is required for any state with a region in non-attainment of U.S. National Ambient Air Quality Standards to demonstrate their approach to achieving compliance with the standard,” says Fiore. “What we found is that small tweaks in, for example, the metrics we use to convey the science findings, can go a long way to making the science more usable, especially when there are detailed policy frameworks in place that must be followed.”

      Now, in 2021, Fiore is part of two tiger teams announced by HAQAST in late September. One team is looking at data to address environmental justice issues, by providing data to assess communities disproportionately affected by environmental health risks. Such information can be used to estimate the benefits of governmental investments in environmental improvements for disproportionately burdened communities. The other team is looking at urban emissions of nitrogen oxides to try to better quantify and communicate uncertainties in the estimates of anthropogenic sources of pollution.

      “For our HAQAST work, we’re looking at not just the estimate of the exposure to air pollutants, or in other words their concentrations,” says Fiore, “but how confident are we in our exposure estimates, which in turn affect our understanding of the public health burden due to exposure. We have stakeholder partners at the New York Department of Health who will pair exposure datasets with health data to help prioritize decisions around public health.

      “I enjoy working with stakeholders who have questions that require science to answer and can make a difference in their decisions.” Fiore says. More

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      Q&A: Cathy Wu on developing algorithms to safely integrate robots into our world

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      Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Data, Systems, and Society. As an undergraduate, Wu won MIT’s toughest robotics competition, and as a graduate student took the University of California at Berkeley’s first-ever course on deep reinforcement learning. Now back at MIT, she’s working to improve the flow of robots in Amazon warehouses under the Science Hub, a new collaboration between the tech giant and the MIT Schwarzman College of Computing. Outside of the lab and classroom, Wu can be found running, drawing, pouring lattes at home, and watching YouTube videos on math and infrastructure via 3Blue1Brown and Practical Engineering. She recently took a break from all of that to talk about her work.

      Q: What put you on the path to robotics and self-driving cars?

      A: My parents always wanted a doctor in the family. However, I’m bad at following instructions and became the wrong kind of doctor! Inspired by my physics and computer science classes in high school, I decided to study engineering. I wanted to help as many people as a medical doctor could.

      At MIT, I looked for applications in energy, education, and agriculture, but the self-driving car was the first to grab me. It has yet to let go! Ninety-four percent of serious car crashes are caused by human error and could potentially be prevented by self-driving cars. Autonomous vehicles could also ease traffic congestion, save energy, and improve mobility.

      I first learned about self-driving cars from Seth Teller during his guest lecture for the course Mobile Autonomous Systems Lab (MASLAB), in which MIT undergraduates compete to build the best full-functioning robot from scratch. Our ball-fetching bot, Putzputz, won first place. From there, I took more classes in machine learning, computer vision, and transportation, and joined Teller’s lab. I also competed in several mobility-related hackathons, including one sponsored by Hubway, now known as Blue Bike.

      Q: You’ve explored ways to help humans and autonomous vehicles interact more smoothly. What makes this problem so hard?

      A: Both systems are highly complex, and our classical modeling tools are woefully insufficient. Integrating autonomous vehicles into our existing mobility systems is a huge undertaking. For example, we don’t know whether autonomous vehicles will cut energy use by 40 percent, or double it. We need more powerful tools to cut through the uncertainty. My PhD thesis at Berkeley tried to do this. I developed scalable optimization methods in the areas of robot control, state estimation, and system design. These methods could help decision-makers anticipate future scenarios and design better systems to accommodate both humans and robots.

      Q: How is deep reinforcement learning, combining deep and reinforcement learning algorithms, changing robotics?

      A: I took John Schulman and Pieter Abbeel’s reinforcement learning class at Berkeley in 2015 shortly after Deepmind published their breakthrough paper in Nature. They had trained an agent via deep learning and reinforcement learning to play “Space Invaders” and a suite of Atari games at superhuman levels. That created quite some buzz. A year later, I started to incorporate reinforcement learning into problems involving mixed traffic systems, in which only some cars are automated. I realized that classical control techniques couldn’t handle the complex nonlinear control problems I was formulating.

      Deep RL is now mainstream but it’s by no means pervasive in robotics, which still relies heavily on classical model-based control and planning methods. Deep learning continues to be important for processing raw sensor data like camera images and radio waves, and reinforcement learning is gradually being incorporated. I see traffic systems as gigantic multi-robot systems. I’m excited for an upcoming collaboration with Utah’s Department of Transportation to apply reinforcement learning to coordinate cars with traffic signals, reducing congestion and thus carbon emissions.

      Q: You’ve talked about the MIT course, 6.007 (Signals and Systems), and its impact on you. What about it spoke to you?

      A: The mindset. That problems that look messy can be analyzed with common, and sometimes simple, tools. Signals are transformed by systems in various ways, but what do these abstract terms mean, anyway? A mechanical system can take a signal like gears turning at some speed and transform it into a lever turning at another speed. A digital system can take binary digits and turn them into other binary digits or a string of letters or an image. Financial systems can take news and transform it via millions of trading decisions into stock prices. People take in signals every day through advertisements, job offers, gossip, and so on, and translate them into actions that in turn influence society and other people. This humble class on signals and systems linked mechanical, digital, and societal systems and showed me how foundational tools can cut through the noise.

      Q: In your project with Amazon you’re training warehouse robots to pick up, sort, and deliver goods. What are the technical challenges?

      A: This project involves assigning robots to a given task and routing them there. [Professor] Cynthia Barnhart’s team is focused on task assignment, and mine, on path planning. Both problems are considered combinatorial optimization problems because the solution involves a combination of choices. As the number of tasks and robots increases, the number of possible solutions grows exponentially. It’s called the curse of dimensionality. Both problems are what we call NP Hard; there may not be an efficient algorithm to solve them. Our goal is to devise a shortcut.

      Routing a single robot for a single task isn’t difficult. It’s like using Google Maps to find the shortest path home. It can be solved efficiently with several algorithms, including Dijkstra’s. But warehouses resemble small cities with hundreds of robots. When traffic jams occur, customers can’t get their packages as quickly. Our goal is to develop algorithms that find the most efficient paths for all of the robots.

      Q: Are there other applications?

      A: Yes. The algorithms we test in Amazon warehouses might one day help to ease congestion in real cities. Other potential applications include controlling planes on runways, swarms of drones in the air, and even characters in video games. These algorithms could also be used for other robotic planning tasks like scheduling and routing.

      Q: AI is evolving rapidly. Where do you hope to see the big breakthroughs coming?

      A: I’d like to see deep learning and deep RL used to solve societal problems involving mobility, infrastructure, social media, health care, and education. Deep RL now has a toehold in robotics and industrial applications like chip design, but we still need to be careful in applying it to systems with humans in the loop. Ultimately, we want to design systems for people. Currently, we simply don’t have the right tools.

      Q: What worries you most about AI taking on more and more specialized tasks?

      A: AI has the potential for tremendous good, but it could also help to accelerate the widening gap between the haves and the have-nots. Our political and regulatory systems could help to integrate AI into society and minimize job losses and income inequality, but I worry that they’re not equipped yet to handle the firehose of AI.

      Q: What’s the last great book you read?

      A: “How to Avoid a Climate Disaster,” by Bill Gates. I absolutely loved the way that Gates was able to take an overwhelmingly complex topic and distill it down into words that everyone can understand. His optimism inspires me to keep pushing on applications of AI and robotics to help avoid a climate disaster. More

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      Q&A: Can the world change course on climate?

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      In this ongoing series on climate issues, MIT faculty, students, and alumni in the humanistic fields share perspectives that are significant for solving climate change and mitigating its myriad social and ecological impacts. Nazli Choucri is a professor of political science and an expert on climate issues, who also focuses on international relations and cyberpolitics. She is the architect and director of the Global System for Sustainable Development, an evolving knowledge networking system centered on sustainability problems and solution strategies. The author and/or editor of 12 books, she is also the founding editor of the MIT Press book series “Global Environmental Accord: Strategies for Sustainability and Institutional Innovation.” Q: The impacts of climate change — including storms, floods, wildfires, and droughts — have the potential to destabilize nations, yet they are not constrained by borders. What international developments most concern you in terms of addressing climate change and its myriad ecological and social impacts?

      A: Climate change is a global issue. By definition, and a long history of practice, countries focus on their own priorities and challenges. Over time, we have seen the gradual development of norms reflecting shared interests, and the institutional arrangements to support and pursue the global good. What concerns me most is that general responses to the climate crisis are being framed in broad terms; the overall pace of change remains perilously slow; and uncertainty remains about operational action and implementation of stated intent. We have just seen the completion of the 26th meeting of states devoted to climate change, the United Nations Climate Change Conference (COP26). In some ways this is positive. Yet, past commitments remain unfulfilled, creating added stress in an already stressful political situation. Industrial countries are uneven in their recognition of, and responses to, climate change. This may signal uncertainty about whether climate matters are sufficiently compelling to call for immediate action. Alternatively, the push for changing course may seem too costly at a time when other imperatives — such as employment, economic growth, or protecting borders — inevitably dominate discourse and decisions. Whatever the cause, the result has been an unwillingness to take strong action. Unfortunately, climate change remains within the domain of “low politics,” although there are signs the issue is making a slow but steady shift to “high politics” — those issues deemed vital to the existence of the state. This means that short-term priorities, such as those noted above, continue to shape national politics and international positions and, by extension, to obscure the existential threat revealed by scientific evidence. As for developing countries, these are overwhelmed by internal challenges, and managing the difficulties of daily life always takes priority over other challenges, however compelling. Long-term thinking is a luxury, but daily bread is a necessity. Non-state actors — including registered nongovernmental organizations, climate organizations, sustainability support groups, activists of various sorts, and in some cases much of civil society — have been left with a large share of the responsibility for educating and convincing diverse constituencies of the consequences of inaction on climate change. But many of these institutions carry their own burdens and struggle to manage current pressures. The international community, through its formal and informal institutions, continues to articulate the perils of climate change and to search for a powerful consensus that can prove effective both in form and in function. The general contours are agreed upon — more or less. But leadership of, for, and by the global collective is elusive and difficult to shape. Most concerning of all is the clear reluctance to address head-on the challenge of planning for changes that we know will occur. The reality that we are all being affected — in different ways and to different degrees — has yet to be sufficiently appreciated by everyone, everywhere. Yet, in many parts of the world, major shifts in climate will create pressures on human settlements, spur forced migrations, or generate social dislocations. Some small island states, for example, may not survive a sea-level surge. Everywhere there is a need to cut emissions, and this means adaptation and/or major changes in economic activity and in lifestyle.The discourse and debate at COP26 reflect all of such persistent features in the international system. So far, the largest achievements center on the common consensus that more must be done to prevent the rise in temperature from creating a global catastrophe. This is not enough, however. Differences remain, and countries have yet to specify what cuts in emissions they are willing to make.Echoes of who is responsible for what remains strong. The thorny matter of the unfulfilled pledge of $100 billion once promised by rich countries to help countries to reduce their emissions remained unresolved. At the same time, however, some important agreements were reached. The United States and China announced they would make greater efforts to cut methane, a powerful greenhouse gas. More than 100 countries agreed to end deforestation. India joined the countries committed to attain zero emissions by 2070. And on matters of finance, countries agreed to a two-year plan to determine how to meet the needs of the most-vulnerable countries. Q: In what ways do you think the tools and insights from political science can advance efforts to address climate change and its impacts?A: I prefer to take a multidisciplinary view of the issues at hand, rather than focus on the tools of political science alone. Disciplinary perspectives can create siloed views and positions that undermine any overall drive toward consensus. The scientific evidence is pointing to, even anticipating, pervasive changes that transcend known and established parameters of social order all across the globe.That said, political science provides important insight, even guidance, for addressing the impacts of climate change in some notable ways. One is understanding the extent to which our formal institutions enable discussion, debate, and decisions about the directions we can take collectively to adapt, adjust, or even depart from the established practices of managing social order.If we consider politics as the allocation of values in terms of who gets what, when, and how, then it becomes clear that the current allocation requires a change in course. Coordination and cooperation across the jurisdictions of sovereign states is foundational for any response to climate change impacts.We have already recognized, and to some extent, developed targets for reducing carbon emissions — a central impact from traditional forms of energy use — and are making notable efforts to shift toward alternatives. This move is an easy one compared to all the work that needs to be done to address climate change. But, in taking this step we have learned quite a bit that might help in creating a necessary consensus for cross-jurisdiction coordination and response.Respecting individuals and protecting life is increasingly recognized as a global value — at least in principle. As we work to change course, new norms will be developed, and political science provides important perspectives on how to establish such norms. We will be faced with demands for institutional design, and these will need to embody our guiding values. For example, having learned to recognize the burdens of inequity, we can establish the value of equity as foundational for our social order both now and as we recognize and address the impacts of climate change.

      Q: You teach a class on “Sustainability Development: Theory and Practice.” Broadly speaking, what are goals of this class? What lessons do you hope students will carry with them into the future?A: The goal of 17.181, my class on sustainability, is to frame as clearly as possible the concept of sustainable development (sustainability) with attention to conceptual, empirical, institutional, and policy issues.The course centers on human activities. Individuals are embedded in complex interactive systems: the social system, the natural environment, and the constructed cyber domain — each with distinct temporal, special, and dynamic features. Sustainability issues intersect with, but cannot be folded into, the impacts of climate change. Sustainability places human beings in social systems at the core of what must be done to respect the imperatives of a highly complex natural environment.We consider sustainability an evolving knowledge domain with attendant policy implications. It is driven by events on the ground, not by revolution in academic or theoretical concerns per se. Overall, sustainable development refers to the process of meeting the needs of current and future generations, without undermining the resilience of the life-supporting properties, the integrity of social systems, or the supports of the human-constructed cyberspace.More specifically, we differentiate among four fundamental dimensions and their necessary conditions:

      (a) ecological systems — exhibiting balance and resilience;(b) economic production and consumption — with equity and efficiency;(c) governance and politics — with participation and responsiveness; and(d) institutional performance — demonstrating adaptation and incorporating feedback.The core proposition is this: If all conditions hold, then the system is (or can be) sustainable. Then, we must examine the critical drivers — people, resources, technology, and their interactions — followed by a review and assessment of evolving policy responses. Then we ask: What are new opportunities?I would like students to carry forward these ideas and issues: what has been deemed “normal” in modern Western societies and in developing societies seeking to emulate the Western model is damaging humans in many ways — all well-known. Yet only recently have alternatives begun to be considered to the traditional economic growth model based on industrialization and high levels of energy use. To make changes, we must first understand the underlying incentives, realities, and choices that shape a whole set of dysfunctional behaviors and outcomes. We then need to delve deep into the driving sources and consequences, and to consider the many ways in which our known “normal” can be adjusted — in theory and in practice. Q: In confronting an issue as formidable as global climate change, what gives you hope?  A: I see a few hopeful signs; among them:The scientific evidence is clear and compelling. We are no longer discussing whether there is climate change, or if we will face major challenges of unprecedented proportions, or even how to bring about an international consensus on the salience of such threats.Climate change has been recognized as a global phenomenon. Imperatives for cooperation are necessary. No one can go it alone. Major efforts have and are being made in world politics to forge action agendas with specific targets.The issue appears to be on the verge of becoming one of “high politics” in the United States.Younger generations are more sensitive to the reality that we are altering the life-supporting properties of our planet. They are generally more educated, skilled, and open to addressing such challenges than their elders.However disappointing the results of COP26 might seem, the global community is moving in the right direction.None of the above points, individually or jointly, translates into an effective response to the known impacts of climate change — let alone the unknown. But, this is what gives me hope.

      Interview prepared by MIT SHASS CommunicationsEditorial, design, and series director: Emily HiestandSenior writer: Kathryn O’Neill More

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      Q&A: More-sustainable concrete with machine learning

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      As a building material, concrete withstands the test of time. Its use dates back to early civilizations, and today it is the most popular composite choice in the world. However, it’s not without its faults. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too.

      Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti’s research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation. Olivetti and Chen, along with their collaborators, have recently teamed up for an MIT-IBM Watson AI Lab project to make concrete more sustainable for the benefit of society, the climate, and the economy.

      Q: What applications does concrete have, and what properties make it a preferred building material?

      Olivetti: Concrete is the dominant building material globally with an annual consumption of 30 billion metric tons. That is over 20 times the next most produced material, steel, and the scale of its use leads to considerable environmental impact, approximately 5-8 percent of global greenhouse gas (GHG) emissions. It can be made locally, has a broad range of structural applications, and is cost-effective. Concrete is a mixture of fine and coarse aggregate, water, cement binder (the glue), and other additives.

      Q: Why isn’t it sustainable, and what research problems are you trying to tackle with this project?

      Olivetti: The community is working on several ways to reduce the impact of this material, including alternative fuels use for heating the cement mixture, increasing energy and materials efficiency and carbon sequestration at production facilities, but one important opportunity is to develop an alternative to the cement binder.

      While cement is 10 percent of the concrete mass, it accounts for 80 percent of the GHG footprint. This impact is derived from the fuel burned to heat and run the chemical reaction required in manufacturing, but also the chemical reaction itself releases CO2 from the calcination of limestone. Therefore, partially replacing the input ingredients to cement (traditionally ordinary Portland cement or OPC) with alternative materials from waste and byproducts can reduce the GHG footprint. But use of these alternatives is not inherently more sustainable because wastes might have to travel long distances, which adds to fuel emissions and cost, or might require pretreatment processes. The optimal way to make use of these alternate materials will be situation-dependent. But because of the vast scale, we also need solutions that account for the huge volumes of concrete needed. This project is trying to develop novel concrete mixtures that will decrease the GHG impact of the cement and concrete, moving away from the trial-and-error processes towards those that are more predictive.

      Chen: If we want to fight climate change and make our environment better, are there alternative ingredients or a reformulation we could use so that less greenhouse gas is emitted? We hope that through this project using machine learning we’ll be able to find a good answer.

      Q: Why is this problem important to address now, at this point in history?

      Olivetti: There is urgent need to address greenhouse gas emissions as aggressively as possible, and the road to doing so isn’t necessarily straightforward for all areas of industry. For transportation and electricity generation, there are paths that have been identified to decarbonize those sectors. We need to move much more aggressively to achieve those in the time needed; further, the technological approaches to achieve that are more clear. However, for tough-to-decarbonize sectors, such as industrial materials production, the pathways to decarbonization are not as mapped out.

      Q: How are you planning to address this problem to produce better concrete?

      Olivetti: The goal is to predict mixtures that will both meet performance criteria, such as strength and durability, with those that also balance economic and environmental impact. A key to this is to use industrial wastes in blended cements and concretes. To do this, we need to understand the glass and mineral reactivity of constituent materials. This reactivity not only determines the limit of the possible use in cement systems but also controls concrete processing, and the development of strength and pore structure, which ultimately control concrete durability and life-cycle CO2 emissions.

      Chen: We investigate using waste materials to replace part of the cement component. This is something that we’ve hypothesized would be more sustainable and economic — actually waste materials are common, and they cost less. Because of the reduction in the use of cement, the final concrete product would be responsible for much less carbon dioxide production. Figuring out the right concrete mixture proportion that makes endurable concretes while achieving other goals is a very challenging problem. Machine learning is giving us an opportunity to explore the advancement of predictive modeling, uncertainty quantification, and optimization to solve the issue. What we are doing is exploring options using deep learning as well as multi-objective optimization techniques to find an answer. These efforts are now more feasible to carry out, and they will produce results with reliability estimates that we need to understand what makes a good concrete.

      Q: What kinds of AI and computational techniques are you employing for this?

      Olivetti: We use AI techniques to collect data on individual concrete ingredients, mix proportions, and concrete performance from the literature through natural language processing. We also add data obtained from industry and/or high throughput atomistic modeling and experiments to optimize the design of concrete mixtures. Then we use this information to develop insight into the reactivity of possible waste and byproduct materials as alternatives to cement materials for low-CO2 concrete. By incorporating generic information on concrete ingredients, the resulting concrete performance predictors are expected to be more reliable and transformative than existing AI models.

      Chen: The final objective is to figure out what constituents, and how much of each, to put into the recipe for producing the concrete that optimizes the various factors: strength, cost, environmental impact, performance, etc. For each of the objectives, we need certain models: We need a model to predict the performance of the concrete (like, how long does it last and how much weight does it sustain?), a model to estimate the cost, and a model to estimate how much carbon dioxide is generated. We will need to build these models by using data from literature, from industry, and from lab experiments.

      We are exploring Gaussian process models to predict the concrete strength, going forward into days and weeks. This model can give us an uncertainty estimate of the prediction as well. Such a model needs specification of parameters, for which we will use another model to calculate. At the same time, we also explore neural network models because we can inject domain knowledge from human experience into them. Some models are as simple as multi-layer perceptions, while some are more complex, like graph neural networks. The goal here is that we want to have a model that is not only accurate but also robust — the input data is noisy, and the model must embrace the noise, so that its prediction is still accurate and reliable for the multi-objective optimization.

      Once we have built models that we are confident with, we will inject their predictions and uncertainty estimates into the optimization of multiple objectives, under constraints and under uncertainties.

      Q: How do you balance cost-benefit trade-offs?

      Chen: The multiple objectives we consider are not necessarily consistent, and sometimes they are at odds with each other. The goal is to identify scenarios where the values for our objectives cannot be further pushed simultaneously without compromising one or a few. For example, if you want to further reduce the cost, you probably have to suffer the performance or suffer the environmental impact. Eventually, we will give the results to policymakers and they will look into the results and weigh the options. For example, they may be able to tolerate a slightly higher cost under a significant reduction in greenhouse gas. Alternatively, if the cost varies little but the concrete performance changes drastically, say, doubles or triples, then this is definitely a favorable outcome.

      Q: What kinds of challenges do you face in this work?

      Chen: The data we get either from industry or from literature are very noisy; the concrete measurements can vary a lot, depending on where and when they are taken. There are also substantial missing data when we integrate them from different sources, so, we need to spend a lot of effort to organize and make the data usable for building and training machine learning models. We also explore imputation techniques that substitute missing features, as well as models that tolerate missing features, in our predictive modeling and uncertainty estimate.

      Q: What do you hope to achieve through this work?

      Chen: In the end, we are suggesting either one or a few concrete recipes, or a continuum of recipes, to manufacturers and policymakers. We hope that this will provide invaluable information for both the construction industry and for the effort of protecting our beloved Earth.

      Olivetti: We’d like to develop a robust way to design cements that make use of waste materials to lower their CO2 footprint. Nobody is trying to make waste, so we can’t rely on one stream as a feedstock if we want this to be massively scalable. We have to be flexible and robust to shift with feedstocks changes, and for that we need improved understanding. Our approach to develop local, dynamic, and flexible alternatives is to learn what makes these wastes reactive, so we know how to optimize their use and do so as broadly as possible. We do that through predictive model development through software we have developed in my group to automatically extract data from literature on over 5 million texts and patents on various topics. We link this to the creative capabilities of our IBM collaborators to design methods that predict the final impact of new cements. If we are successful, we can lower the emissions of this ubiquitous material and play our part in achieving carbon emissions mitigation goals.

      Other researchers involved with this project include Stefanie Jegelka, the X-Window Consortium Career Development Associate Professor in the MIT Department of Electrical Engineering and Computer Science; Richard Goodwin, IBM principal researcher; Soumya Ghosh, MIT-IBM Watson AI Lab research staff member; and Kristen Severson, former research staff member. Collaborators included Nghia Hoang, former research staff member with MIT-IBM Watson AI Lab and IBM Research; and Jeremy Gregory, research scientist in the MIT Department of Civil and Environmental Engineering and executive director of the MIT Concrete Sustainability Hub.

      This research is supported by the MIT-IBM Watson AI Lab. More

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      Design’s new frontier

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      In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

      “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

      Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

      In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

      Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

      Computational design

      Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

      “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

      For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

      To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

      Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

      Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

      “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

      Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

      Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

      “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

      Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

      Personal fabrication

      Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

      “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

      Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

      First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

      To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

      “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

      Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

      Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

      “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

      Computational fluid dynamics and participatory design

      Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

      As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

      “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

      After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

      The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

      Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

      “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

      These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

      Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

      Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

      The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

      Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

      “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

      While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

      Designing intelligent systems

      When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

      Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

      “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

      For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

      To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

      “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

      Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

      “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

      To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

      In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

      “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

      Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

      Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

      “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

      Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

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      MIT collaborates with Biogen on three-year, $7 million initiative to address climate, health, and equity

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      MIT and Biogen have announced that they will collaborate with the goal to accelerate the science and action on climate change to improve human health. This collaboration is supported by a three-year, $7 million commitment from the company and the Biogen Foundation. The biotechnology company, headquartered in Cambridge, Massachusetts’ Kendall Square, discovers and develops therapies for people living with serious neurological diseases.

      “We have long believed it is imperative for Biogen to make the fight against climate change central to our long-term corporate responsibility commitments. Through this collaboration with MIT, we aim to identify and share innovative climate solutions that will deliver co-benefits for both health and equity,” says Michel Vounatsos, CEO of Biogen. “We are also proud to support the MIT Museum, which promises to make world-class science and education accessible to all, and honor Biogen co-founder Phillip A. Sharp with a dedication inside the museum that recognizes his contributions to its development.”

      Biogen and the Biogen Foundation are supporting research and programs across a range of areas at MIT.

      Advancing climate, health, and equity

      The first such effort involves new work within the MIT Joint Program on the Science and Policy of Global Change to establish a state-of-the-art integrated model of climate and health aimed at identifying targets that deliver climate and health co-benefits.

      “Evidence suggests that not all climate-related actions deliver equal health benefits, yet policymakers, planners, and stakeholders traditionally lack the tools to consider how decisions in one arena impact the other,” says C. Adam Schlosser, deputy director of the MIT Joint Program. “Biogen’s collaboration with the MIT Joint Program — and its support of a new distinguished Biogen Fellow who will develop the new climate/health model — will accelerate our efforts to provide decision-makers with these tools.”

      Biogen is also supporting the MIT Technology and Policy Program’s Research to Policy Engagement Initiative to infuse human health as a key new consideration in decision-making on the best pathways forward to address the global climate crisis, and bridge the knowledge-to-action gap by connecting policymakers, researchers, and diverse stakeholders. As part of this work, Biogen is underwriting a distinguished Biogen Fellow to advance new research on climate, health, and equity.

      “Our work with Biogen has allowed us to make progress on key questions that matter to human health and well-being under climate change,” says Noelle Eckley Selin, who directs the MIT Technology and Policy Program and is a professor in the MIT Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences. “Further, their support of the Research to Policy Engagement Initiative helps all of our research become more effective in making change.”

      In addition, Biogen has joined 13 other companies in the MIT Climate and Sustainability Consortium (MCSC), which is supporting faculty and student research and developing impact pathways that present a range of actionable steps that companies can take — within and across industries — to advance progress toward climate targets.

      “Biogen joining the MIT Climate and Sustainability Consortium represents our commitment to working with member companies across a diverse range of industries, an approach that aims to drive changes swift and broad enough to match the scale of the climate challenge,” says Jeremy Gregory, executive director of the MCSC. “We are excited to welcome a member from the biotechnology space and look forward to harnessing Biogen’s perspectives as we continue to collaborate and work together with the MIT community in exciting and meaningful ways.”

      Making world-class science and education available to MIT Museum visitors

      Support from Biogen will honor Nobel laureate, MIT Institute professor, and Biogen co-founder Phillip A. Sharp with a named space inside the new Kendall Square location of the MIT Museum, set to open in spring 2022. Biogen also is supporting one of the museum’s opening exhibitions, “Essential MIT,” with a section focused on solving real-world problems such as climate change. It is also providing programmatic support for the museum’s Life Sciences Maker Engagement Program.

      “Phil has provided fantastic support to the MIT Museum for more than a decade as an advisory board member and now as board chair, and he has been deeply involved in plans for the new museum at Kendall Square,” says John Durant, the Mark R. Epstein (Class of 1963) Director of the museum. “Seeing his name on the wall will be a constant reminder of his key role in this development, as well as a mark of our gratitude.”

      Inspiring and empowering the next generation of scientists

      Biogen funding is also being directed to engage the next generation of scientists through support for the Biogen-MIT Biotech in Action: Virtual Lab, a program designed to foster a love of science among diverse and under-served student populations.

      Biogen’s support is part of its Healthy Climate, Healthy Lives initiative, a $250 million, 20-year commitment to eliminate fossil fuels across its operations and collaborate with renowned institutions to advance the science of climate and health and support under-served communities. Additional support is provided by the Biogen Foundation to further its long-standing focus on providing students with equitable access to outstanding science education. 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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      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