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      MIT announces five flagship projects in first-ever Climate Grand Challenges competition

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      MIT today announced the five flagship projects selected in its first-ever Climate Grand Challenges competition. These multiyear projects will define a dynamic research agenda focused on unraveling some of the toughest unsolved climate problems and bringing high-impact, science-based solutions to the world on an accelerated basis.

      Representing the most promising concepts to emerge from the two-year competition, the five flagship projects will receive additional funding and resources from MIT and others to develop their ideas and swiftly transform them into practical solutions at scale.

      “Climate Grand Challenges represents a whole-of-MIT drive to develop game-changing advances to confront the escalating climate crisis, in time to make a difference,” says MIT President L. Rafael Reif. “We are inspired by the creativity and boldness of the flagship ideas and by their potential to make a significant contribution to the global climate response. But given the planet-wide scale of the challenge, success depends on partnership. We are eager to work with visionary leaders in every sector to accelerate this impact-oriented research, implement serious solutions at scale, and inspire others to join us in confronting this urgent challenge for humankind.”

      Brief descriptions of the five Climate Grand Challenges flagship projects are provided below.

      Bringing Computation to the Climate Challenge

      This project leverages advances in artificial intelligence, machine learning, and data sciences to improve the accuracy of climate models and make them more useful to a variety of stakeholders — from communities to industry. The team is developing a digital twin of the Earth that harnesses more data than ever before to reduce and quantify uncertainties in climate projections.

      Research leads: Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in the Department of Earth, Atmospheric and Planetary Sciences, and director of the Program in Atmospheres, Oceans, and Climate; and Noelle Eckley Selin, director of the Technology and Policy Program and professor with a joint appointment in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences

      Center for Electrification and Decarbonization of Industry

      This project seeks to reinvent and electrify the processes and materials behind hard-to-decarbonize industries like steel, cement, ammonia, and ethylene production. A new innovation hub will perform targeted fundamental research and engineering with urgency, pushing the technological envelope on electricity-driven chemical transformations.

      Research leads: Yet-Ming Chiang, the Kyocera Professor of Materials Science and Engineering, and Bilge Yıldız, the Breene M. Kerr Professor in the Department of Nuclear Science and Engineering and professor in the Department of Materials Science and Engineering

      Preparing for a new world of weather and climate extremes

      This project addresses key gaps in knowledge about intensifying extreme events such as floods, hurricanes, and heat waves, and quantifies their long-term risk in a changing climate. The team is developing a scalable climate-change adaptation toolkit to help vulnerable communities and low-carbon energy providers prepare for these extreme weather events.

      Research leads: Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in the Department of Earth, Atmospheric and Planetary Sciences and co-director of the MIT Lorenz Center; Miho Mazereeuw, associate professor of architecture and urbanism in the Department of Architecture and director of the Urban Risk Lab; and Paul O’Gorman, professor in the Program in Atmospheres, Oceans, and Climate in the Department of Earth, Atmospheric and Planetary Sciences

      The Climate Resilience Early Warning System

      The CREWSnet project seeks to reinvent climate change adaptation with a novel forecasting system that empowers underserved communities to interpret local climate risk, proactively plan for their futures incorporating resilience strategies, and minimize losses. CREWSnet will initially be demonstrated in southwestern Bangladesh, serving as a model for similarly threatened regions around the world.

      Research leads: John Aldridge, assistant leader of the Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory, and Elfatih Eltahir, the H.M. King Bhumibol Professor of Hydrology and Climate in the Department of Civil and Environmental Engineering

      Revolutionizing agriculture with low-emissions, resilient crops

      This project works to revolutionize the agricultural sector with climate-resilient crops and fertilizers that have the ability to dramatically reduce greenhouse gas emissions from food production.

      Research lead: Christopher Voigt, the Daniel I.C. Wang Professor in the Department of Biological Engineering

      “As one of the world’s leading institutions of research and innovation, it is incumbent upon MIT to draw on our depth of knowledge, ingenuity, and ambition to tackle the hard climate problems now confronting the world,” says Richard Lester, MIT associate provost for international activities. “Together with collaborators across industry, finance, community, and government, the Climate Grand Challenges teams are looking to develop and implement high-impact, path-breaking climate solutions rapidly and at a grand scale.”

      The initial call for ideas in 2020 yielded nearly 100 letters of interest from almost 400 faculty members and senior researchers, representing 90 percent of MIT departments. After an extensive evaluation, 27 finalist teams received a total of $2.7 million to develop comprehensive research and innovation plans. The projects address four broad research themes:

      To select the winning projects, research plans were reviewed by panels of international experts representing relevant scientific and technical domains as well as experts in processes and policies for innovation and scalability.

      “In response to climate change, the world really needs to do two things quickly: deploy the solutions we already have much more widely, and develop new solutions that are urgently needed to tackle this intensifying threat,” says Maria Zuber, MIT vice president for research. “These five flagship projects exemplify MIT’s strong determination to bring its knowledge and expertise to bear in generating new ideas and solutions that will help solve the climate problem.”

      “The Climate Grand Challenges flagship projects set a new standard for inclusive climate solutions that can be adapted and implemented across the globe,” says MIT Chancellor Melissa Nobles. “This competition propels the entire MIT research community — faculty, students, postdocs, and staff — to act with urgency around a worsening climate crisis, and I look forward to seeing the difference these projects can make.”

      “MIT’s efforts on climate research amid the climate crisis was a primary reason that I chose to attend MIT, and remains a reason that I view the Institute favorably. MIT has a clear opportunity to be a thought leader in the climate space in our own MIT way, which is why CGC fits in so well,” says senior Megan Xu, who served on the Climate Grand Challenges student committee and is studying ways to make the food system more sustainable.

      The Climate Grand Challenges competition is a key initiative of “Fast Forward: MIT’s Climate Action Plan for the Decade,” which the Institute published in May 2021. Fast Forward outlines MIT’s comprehensive plan for helping the world address the climate crisis. It consists of five broad 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. More

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      3 Questions: Fotini Christia on racial equity and data science

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      Fotini Christia is the Ford International Professor in the Social Sciences in the Department of Political Science, associate director of the Institute for Data, Systems, and Society (IDSS), and director of the Sociotechnical Systems Research Center (SSRC). Her research interests include issues of conflict and cooperation in the Muslim world, and she has conducted fieldwork in Afghanistan, Bosnia, Iran, the Palestinian Territories, Syria, and Yemen. She has co-organized the IDSS Research Initiative on Combatting Systemic Racism (ICSR), which works to bridge the social sciences, data science, and computation by bringing researchers from these disciplines together to address systemic racism across housing, health care, policing, education, employment, and other sectors of society.

      Q: What is the IDSS/ICSR approach to systemic racism research?

      A: The Research Initiative on Combatting Systemic Racism (ICSR) aims to seed and coordinate cross-disciplinary research to identify and overcome racially discriminatory processes and outcomes across a range of U.S. institutions and policy domains.

      Building off the extensive social science literature on systemic racism, the focus of this research initiative is to use big data to develop and harness computational tools that can help effect structural and normative change toward racial equity.

      The initiative aims to create a visible presence at MIT for cutting-edge computational research with a racial equity lens, across societal domains that will attract and train students and scholars.

      The steering committee for this research initiative is composed of underrepresented minority faculty members from across MIT’s five schools and the MIT Schwarzman College of Computing. Members will serve as close advisors to the initiative as well as share the findings of our work beyond MIT’s campus. MIT Chancellor Melissa Nobles heads this committee.

      Q: What role can data science play in helping to effect change toward racial equity?

      A: Existing work has shown racial discrimination in the job market, in the criminal justice system, as well as in education, health care, and access to housing, among other places. It has also underlined how algorithms could further entrench such bias — be it in training data or in the people who build them. Data science tools can not only help identify, but also contribute to, proposing fixes on racially inequitable outcomes that result from implicit or explicit biases in governing institutional practices in the public and private sector, and more recently from the use of AI and algorithmic methods in decision-making.

      To that effect, this initiative will produce research that explores and collects the relevant big data across domains, while paying attention to the ways such data are collected, and focus on improving and developing data-driven computational tools to address racial disparities in structures and institutions that have reproduced racially discriminatory outcomes in American society.

      The strong correlation between race, class, educational attainment, and various attitudes and behaviors in the American context can make it extremely difficult to rule out the influence of confounding factors. Thus, a key motivation for our research initiative is to highlight the importance of causal analysis using computational methods, and focus on understanding the opportunities of big data and algorithmic decision-making to address racial inequities and promote racial justice — beyond de-biasing algorithms. The intent is to also codify methodologies on equity-informed research practices and produce tools that are clear on the quantifiable expected social costs and benefits, as well as on the downstream effects on systemic racism more broadly.

      Q: What are some ways that the ICSR might conduct or follow-up on research seeking real-world impact or policy change?

      A: This type of research has ethical and societal considerations at its core, especially as they pertain to historically disadvantaged groups in the U.S., and will be coordinated with and communicated to local stakeholders to drive relevant policy decisions. This initiative intends to establish connections to URM [underrepresented minority] researchers and students at underrepresented universities and to directly collaborate with them on these research efforts. To that effect, we are leveraging existing programs such as the MIT Summer Research Program (MSRP).

      To ensure that our research targets the right problems bringing a racial equity lens with an interest to effect policy change, we will also connect with community organizations in minority neighborhoods who often bear the brunt of the direct and indirect effects of systemic racism, as well as with local government offices that work to address inequity in service provision in these communities. Our intent is to directly engage IDSS students with these organizations to help develop and test algorithmic tools for racial equity. More

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      3 Questions: What a single car can say about traffic

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      Vehicle traffic has long defied description. Once measured roughly through visual inspection and traffic cameras, new smartphone crowdsourcing tools are now quantifying traffic far more precisely. This popular method, however, also presents a problem: Accurate measurements require a lot of data and users.

      Meshkat Botshekan, an MIT PhD student in civil and environmental engineering and research assistant at the MIT Concrete Sustainability Hub, has sought to expand on crowdsourcing methods by looking into the physics of traffic. During his time as a doctoral candidate, he has helped develop Carbin, a smartphone-based roadway crowdsourcing tool created by MIT CSHub and the University of Massachusetts Dartmouth, and used its data to offer more insight into the physics of traffic — from the formation of traffic jams to the inference of traffic phase and driving behavior. Here, he explains how recent findings can allow smartphones to infer traffic properties from the measurements of a single vehicle.  

      Q: Numerous navigation apps already measure traffic. Why do we need alternatives?

      A: Traffic characteristics have always been tough to measure. In the past, visual inspection and cameras were used to produce traffic metrics. So, there’s no denying that today’s navigation tools apps offer a superior alternative. Yet even these modern tools have gaps.

      Chief among them is their dependence on spatially distributed user counts: Essentially, these apps tally up their users on road segments to estimate the density of traffic. While this approach may seem adequate, it is both vulnerable to manipulation, as demonstrated in some viral videos, and requires immense quantities of data for reliable estimates. Processing these data is so time- and resource-intensive that, despite their availability, they can’t be used to quantify traffic effectively across a whole road network. As a result, this immense quantity of traffic data isn’t actually optimal for traffic management.

      Q: How could new technologies improve how we measure traffic?

      A: New alternatives have the potential to offer two improvements over existing methods: First, they can extrapolate far more about traffic with far fewer data. Second, they can cost a fraction of the price while offering a far simpler method of data collection. Just like Waze and Google Maps, they rely on crowdsourcing data from users. Yet, they are grounded in the incorporation of high-level statistical physics into data analysis.

      For instance, the Carbin app, which we are developing in collaboration with UMass Dartmouth, applies principles of statistical physics to existing traffic models to entirely forgo the need for user counts. Instead, it can infer traffic density and driver behavior using the input of a smartphone mounted in single vehicle.

      The method at the heart of the app, which was published last fall in Physical Review E, treats vehicles like particles in a many-body system. Just as the behavior of a closed many-body system can be understood through observing the behavior of an individual particle relying on the ergodic theorem of statistical physics, we can characterize traffic through the fluctuations in speed and position of a single vehicle across a road. As a result, we can infer the behavior and density of traffic on a segment of a road.

      As far less data is required, this method is more rapid and makes data management more manageable. But most importantly, it also has the potential to make traffic data less expensive and accessible to those that need it.

      Q: Who are some of the parties that would benefit from new technologies?

      A: More accessible and sophisticated traffic data would benefit more than just drivers seeking smoother, faster routes. It would also enable state and city departments of transportation (DOTs) to make local and collective interventions that advance the critical transportation objectives of equity, safety, and sustainability.

      As a safety solution, new data collection technologies could pinpoint dangerous driving conditions on a much finer scale to inform improved traffic calming measures. And since socially vulnerable communities experience traffic violence disproportionately, these interventions would have the added benefit of addressing pressing equity concerns. 

      There would also be an environmental benefit. DOTs could mitigate vehicle emissions by identifying minute deviations in traffic flow. This would present them with more opportunities to mitigate the idling and congestion that generate excess fuel consumption.  

      As we’ve seen, these three challenges have become increasingly acute, especially in urban areas. Yet, the data needed to address them exists already — and is being gathered by smartphones and telematics devices all over the world. So, to ensure a safer, more sustainable road network, it will be crucial to incorporate these data collection methods into our decision-making. 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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      in Data Management & Statistics

      3 Questions: Kalyan Veeramachaneni on hurdles preventing fully automated machine learning

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      The proliferation of big data across domains, from banking to health care to environmental monitoring, has spurred increasing demand for machine learning tools that help organizations make decisions based on the data they gather.

      That growing industry demand has driven researchers to explore the possibilities of automated machine learning (AutoML), which seeks to automate the development of machine learning solutions in order to make them accessible for nonexperts, improve their efficiency, and accelerate machine learning research. For example, an AutoML system might enable doctors to use their expertise interpreting electroencephalography (EEG) results to build a model that can predict which patients are at higher risk for epilepsy — without requiring the doctors to have a background in data science.

      Yet, despite more than a decade of work, researchers have been unable to fully automate all steps in the machine learning development process. Even the most efficient commercial AutoML systems still require a prolonged back-and-forth between a domain expert, like a marketing manager or mechanical engineer, and a data scientist, making the process inefficient.

      Kalyan Veeramachaneni, a principal research scientist in the MIT Laboratory for Information and Decision Systems who has been studying AutoML since 2010, has co-authored a paper in the journal ACM Computing Surveys that details a seven-tiered schematic to evaluate AutoML tools based on their level of autonomy.

      A system at level zero has no automation and requires a data scientist to start from scratch and build models by hand, while a tool at level six is completely automated and can be easily and effectively used by a nonexpert. Most commercial systems fall somewhere in the middle.

      Veeramachaneni spoke with MIT News about the current state of AutoML, the hurdles that prevent truly automatic machine learning systems, and the road ahead for AutoML researchers.

      Q: How has automatic machine learning evolved over the past decade, and what is the current state of AutoML systems?

      A: In 2010, we started to see a shift, with enterprises wanting to invest in getting value out of their data beyond just business intelligence. So then came the question, maybe there are certain things in the development of machine learning-based solutions that we can automate? The first iteration of AutoML was to make our own jobs as data scientists more efficient. Can we take away the grunt work that we do on a day-to-day basis and automate that by using a software system? That area of research ran its course until about 2015, when we realized we still weren’t able to speed up this development process.

      Then another thread emerged. There are a lot of problems that could be solved with data, and they come from experts who know those problems, who live with them on a daily basis. These individuals have very little to do with machine learning or software engineering. How do we bring them into the fold? That is really the next frontier.

      There are three areas where these domain experts have strong input in a machine learning system. The first is defining the problem itself and then helping to formulate it as a prediction task to be solved by a machine learning model. Second, they know how the data have been collected, so they also know intuitively how to process that data. And then third, at the end, machine learning models only give you a very tiny part of a solution — they just give you a prediction. The output of a machine learning model is just one input to help a domain expert get to a decision or action.

      Q: What steps of the machine learning pipeline are the most difficult to automate, and why has automating them been so challenging?

      A: The problem-formulation part is extremely difficult to automate. For example, if I am a researcher who wants to get more government funding, and I have a lot of data about the content of the research proposals that I write and whether or not I receive funding, can machine learning help there? We don’t know yet. In problem formulation, I use my domain expertise to translate the problem into something that is more tangible to predict, and that requires somebody who knows the domain very well. And he or she also knows how to use that information post-prediction. That problem is refusing to be automated.

      There is one part of problem-formulation that could be automated. It turns out that we can look at the data and mathematically express several possible prediction tasks automatically. Then we can share those prediction tasks with the domain expert to see if any of them would help in the larger problem they are trying to tackle. Then once you pick the prediction task, there are a lot of intermediate steps you do, including feature engineering, modeling, etc., that are very mechanical steps and easy to automate.

      But defining the prediction tasks has typically been a collaborative effort between data scientists and domain experts because, unless you know the domain, you can’t translate the domain problem into a prediction task. And then sometimes domain experts don’t know what is meant by “prediction.” That leads to the major, significant back and forth in the process. If you automate that step, then machine learning penetration and the use of data to create meaningful predictions will increase tremendously.

      Then what happens after the machine learning model gives a prediction? We can automate the software and technology part of it, but at the end of the day, it is root cause analysis and human intuition and decision making. We can augment them with a lot of tools, but we can’t fully automate that.

      Q: What do you hope to achieve with the seven-tiered framework for evaluating AutoML systems that you outlined in your paper?

      A: My hope is that people start to recognize that some levels of automation have already been achieved and some still need to be tackled. In the research community, we tend to focus on what we are comfortable with. We have gotten used to automating certain steps, and then we just stick to it. Automating these other parts of the machine learning solution development is very important, and that is where the biggest bottlenecks remain.

      My second hope is that researchers will very clearly understand what domain expertise means. A lot of this AutoML work is still being conducted by academics, and the problem is that we often don’t do applied work. There is not a crystal-clear definition of what a domain expert is and in itself, “domain expert,” is a very nebulous phrase. What we mean by domain expert is the expert in the problem you are trying to solve with machine learning. And I am hoping that everyone unifies around that because that would make things so much clearer.

      I still believe that we are not able to build that many models for that many problems, but even for the ones that we are building, the majority of them are not getting deployed and used in day-to-day life. The output of machine learning is just going to be another data point, an augmented data point, in someone’s decision making. How they make those decisions, based on that input, how that will change their behavior, and how they will adapt their style of working, that is still a big, open question. Once we automate everything, that is what’s next.

      We have to determine what has to fundamentally change in the day-to-day workflow of someone giving loans at a bank, or an educator trying to decide whether he or she should change the assignments in an online class. How are they going to use machine learning’s outputs? We need to focus on the fundamental things we have to build out to make machine learning more usable. More