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      Bridging the gap between preschool policy, practice, and research

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      Preschool in the United States has grown dramatically in the past several decades. From 1970 to 2018, preschool enrollment increased from 38 percent to 64 percent of eligible students. Fourteen states are currently discussing preschool expansion, with seven likely to pass some form of universal eligibility within the next calendar year. Amid this expansion, families, policymakers, and practitioners want to better understand preschools’ impacts and the factors driving preschool quality. 

      To address these and other questions, MIT Blueprint Labs recently held a Preschool Research Convening that brought researchers, funders, practitioners, and policymakers to Nashville, Tennessee, to discuss the future of preschool research. Parag Pathak, the Class of 1922 Professor of Economics at MIT and a Blueprint Labs co-founder and director, opened by sharing the goals of the convening: “Our goals for the next two days are to identify pressing, unanswered research questions and connect researchers, practitioners, policymakers, and funders. We also hope to craft a compelling research agenda.”

      Pathak added, “Given preschool expansion nationwide, we believe now is the moment to centralize our efforts and create knowledge to inform pressing decisions. We aim to generate rigorous preschool research that will lead to higher-quality and more equitable preschool.”

      Over 75 participants hailing from universities, early childhood education organizations, school districts, state education departments, and national policy organizations attended the convening, held Nov. 13-14. Through panels, presentations, and conversations, participants discussed essential subjects in the preschool space, built the foundations for valuable partnerships, and formed an actionable and inclusive research agenda.

      Research presented

      Among research works presented was a recent paper by Blueprint Labs affiliate Jesse Bruhn, an assistant professor of economics at Brown University and co-author Emily Emick, also of Brown, reviewing the state of lottery-based preschool research. They found that random evaluations from the past 60 years demonstrate that preschool improves children’s short-run academic outcomes, but those effects fade over time. However, positive impacts re-emerge in the long term through improved outcomes like high school graduation and college enrollment. Limited rigorous research studies children’s behavioral outcomes or the factors that lead to high-quality preschool, though trends from preliminary research suggest that full-day programs, language immersion programs, and specific curricula may benefit children.  

      An earlier Blueprint Labs study that was also presented at the convening is the only recent lottery-based study to provide insight on preschool’s long-term impacts. The work, conducted by Pathak and two others, reveals that enrolling in Boston Public Schools’ universal preschool program boosts children’s likelihood of graduating high school and enrolling in college. Yet, the preschool program had little detectable impact on elementary, middle, and high school state standardized test scores. Students who attended Boston preschool were less likely to be suspended or incarcerated in high school. However, research on preschool’s impacts on behavioral outcomes is limited; it remains an important area for further study. Future work could also fill in other gaps in research, such as access, alternative measures of student success, and variation across geographic contexts and student populations.

      More data sought

      State policy leaders also spoke at the event, including Lisa Roy, executive director of the Colorado Department of Early Childhood, and Sarah Neville-Morgan, deputy superintendent in the Opportunities for All Branch at the California Department of Education. Local practitioners, such as Elsa Holguín, president and CEO of the Denver Preschool Program, and Kristin Spanos, CEO of First 5 Alameda County, as well as national policy leaders including Lauren Hogan, managing director of policy and professional advancement at the National Association for the Education of Young Children, also shared their perspectives. 

      In panel discussions held throughout the kickoff, practitioners, policymakers, and researchers shared their perspectives on pressing questions for future research, including: What practices define high-quality preschool? How does preschool affect family systems and the workforce? How can we expand measures of effectiveness to move beyond traditional assessments? What can we learn from preschool’s differential impacts across time, settings, models, and geographies?

      Panelists also discussed the need for reliable data, sharing that “the absence of data allows the status quo to persist.” Several sessions focused on involving diverse stakeholders in the research process, highlighting the need for transparency, sensitivity to community contexts, and accessible communication about research findings.

      On the second day of the Preschool Research Convening, Pathak shared with attendees, “One of our goals… is to forge connections between all of you in this room and support new partnerships between researchers and practitioners. We hope your conversations are the launching pad for future collaborations.” Jason Sachs, the deputy director of early learning at the Bill and Melinda Gates Foundation and former director of early childhood at Boston Public Schools, provided closing remarks.

      The convening laid the groundwork for a research agenda and new research partnerships that can help answer questions about what works, in what context, for which kids, and under which conditions. Answers to these questions will be fundamental to ensure preschool expands in the most evidence-informed and equitable way possible.

      With this goal in mind, Blueprint Labs aims to create a new Preschool Research Collaborative to equip practitioners, policymakers, funders, and researchers with rigorous, actionable evidence on preschool performance. Pathak states, “We hope this collaborative will foster evidence-based decision-making that improves children’s short- and long-term outcomes.” The connections and research agenda formed at the Preschool Research Convening are the first steps toward achieving that goal. More

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      Leveraging language to understand machines

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      Natural language conveys ideas, actions, information, and intent through context and syntax; further, there are volumes of it contained in databases. This makes it an excellent source of data to train machine-learning systems on. Two master’s of engineering students in the 6A MEng Thesis Program at MIT, Irene Terpstra ’23 and Rujul Gandhi ’22, are working with mentors in the MIT-IBM Watson AI Lab to use this power of natural language to build AI systems.

      As computing is becoming more advanced, researchers are looking to improve the hardware that they run on; this means innovating to create new computer chips. And, since there is literature already available on modifications that can be made to achieve certain parameters and performance, Terpstra and her mentors and advisors Anantha Chandrakasan, MIT School of Engineering dean and the Vannevar Bush Professor of Electrical Engineering and Computer Science, and IBM’s researcher Xin Zhang, are developing an AI algorithm that assists in chip design.

      “I’m creating a workflow to systematically analyze how these language models can help the circuit design process. What reasoning powers do they have, and how can it be integrated into the chip design process?” says Terpstra. “And then on the other side, if that proves to be useful enough, [we’ll] see if they can automatically design the chips themselves, attaching it to a reinforcement learning algorithm.”

      To do this, Terpstra’s team is creating an AI system that can iterate on different designs. It means experimenting with various pre-trained large language models (like ChatGPT, Llama 2, and Bard), using an open-source circuit simulator language called NGspice, which has the parameters of the chip in code form, and a reinforcement learning algorithm. With text prompts, researchers will be able to query how the physical chip should be modified to achieve a certain goal in the language model and produced guidance for adjustments. This is then transferred into a reinforcement learning algorithm that updates the circuit design and outputs new physical parameters of the chip.

      “The final goal would be to combine the reasoning powers and the knowledge base that is baked into these large language models and combine that with the optimization power of the reinforcement learning algorithms and have that design the chip itself,” says Terpstra.

      Rujul Gandhi works with the raw language itself. As an undergraduate at MIT, Gandhi explored linguistics and computer sciences, putting them together in her MEng work. “I’ve been interested in communication, both between just humans and between humans and computers,” Gandhi says.

      Robots or other interactive AI systems are one area where communication needs to be understood by both humans and machines. Researchers often write instructions for robots using formal logic. This helps ensure that commands are being followed safely and as intended, but formal logic can be difficult for users to understand, while natural language comes easily. To ensure this smooth communication, Gandhi and her advisors Yang Zhang of IBM and MIT assistant professor Chuchu Fan are building a parser that converts natural language instructions into a machine-friendly form. Leveraging the linguistic structure encoded by the pre-trained encoder-decoder model T5, and a dataset of annotated, basic English commands for performing certain tasks, Gandhi’s system identifies the smallest logical units, or atomic propositions, which are present in a given instruction.

      “Once you’ve given your instruction, the model identifies all the smaller sub-tasks you want it to carry out,” Gandhi says. “Then, using a large language model, each sub-task can be compared against the available actions and objects in the robot’s world, and if any sub-task can’t be carried out because a certain object is not recognized, or an action is not possible, the system can stop right there to ask the user for help.”

      This approach of breaking instructions into sub-tasks also allows her system to understand logical dependencies expressed in English, like, “do task X until event Y happens.” Gandhi uses a dataset of step-by-step instructions across robot task domains like navigation and manipulation, with a focus on household tasks. Using data that are written just the way humans would talk to each other has many advantages, she says, because it means a user can be more flexible about how they phrase their instructions.

      Another of Gandhi’s projects involves developing speech models. In the context of speech recognition, some languages are considered “low resource” since they might not have a lot of transcribed speech available, or might not have a written form at all. “One of the reasons I applied to this internship at the MIT-IBM Watson AI Lab was an interest in language processing for low-resource languages,” she says. “A lot of language models today are very data-driven, and when it’s not that easy to acquire all of that data, that’s when you need to use the limited data efficiently.” 

      Speech is just a stream of sound waves, but humans having a conversation can easily figure out where words and thoughts start and end. In speech processing, both humans and language models use their existing vocabulary to recognize word boundaries and understand the meaning. In low- or no-resource languages, a written vocabulary might not exist at all, so researchers can’t provide one to the model. Instead, the model can make note of what sound sequences occur together more frequently than others, and infer that those might be individual words or concepts. In Gandhi’s research group, these inferred words are then collected into a pseudo-vocabulary that serves as a labeling method for the low-resource language, creating labeled data for further applications.

      The applications for language technology are “pretty much everywhere,” Gandhi says. “You could imagine people being able to interact with software and devices in their native language, their native dialect. You could imagine improving all the voice assistants that we use. You could imagine it being used for translation or interpretation.” More

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      “MIT can give you ‘superpowers’”

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      Speaking at the virtual MITx MicroMasters Program Joint Completion Celebration last summer, Diogo da Silva Branco Magalhães described watching a Spider-Man movie with his 8-year-old son and realizing that his son thought MIT was a fictional entity that existed only in the Marvel universe.

      “I had to tell him that MIT also exists in the real world, and that some of the programs are available online for everyone,” says da Silva Branco Magalhães, who earned his credential in the MicroMasters in Statistics and Data Science program. “You don’t need to be a superhero to participate in an MIT program, but MIT can give you ‘superpowers.’ In my case, the superpower that I was looking to acquire was a better understanding of the key technologies that are shaping the future of transportation.

      Part of MIT Open Learning, the MicroMasters programs have drawn in almost 1.4 million learners, spanning nearly every country in the world. More than 7,500 people have earned their credentials across the MicroMasters programs, including: Statistics and Data Science; Supply Chain Management; Data, Economics, and Design of Policy; Principles of Manufacturing; and Finance. 

      Earning his MicroMasters credential not only gave da Silva Branco Magalhães a strong foundation to tackle more complex transportation problems, but it also opened the door to pursuing an accelerated graduate degree via a Northwestern University online program.

      Learners who earn their MicroMasters credentials gain the opportunity to apply to and continue their studies at a pathway school. The MicroMasters in Statistics and Data Science credential can be applied as credit for a master’s program at more than 30 universities, as well as MIT’s PhD Program in Social and Engineering Systems. Da Silva Branco Magalhães, originally from Portugal and now based in Australia, seized this opportunity and enrolled in Northwestern University’s Master’s in Data Science for MIT MicroMasters Credential Holders. 

      The pathway to an enhanced career

      The pathway model launched in 2016 with the MicroMasters in Supply Chain Management. Now, there are over 50 pathway institutions that offer more than 100 different programs for master’s degrees. With pathway institutions located around the world, MicroMasters credential holders can obtain master’s degrees from local residential or virtual programs, at a location convenient to them. They can receive credit for their MicroMasters courses upon acceptance, providing flexibility for online programs and also shortening the time needed on site for residential programs.

      “The pathways expand opportunities for learners, and also help universities attract a broader range of potential students, which can enrich their programs,” says Dana Doyle, senior director for the MicroMasters Program at MIT Open Learning. “This is a tangible way we can achieve our mission of expanding education access.”

      Da Silva Branco Magalhães began the MicroMasters in Statistics and Data Science program in 2020, ultimately completing the program in 2022.

      “After having worked for 20 years in the transportation sector in various roles, I realized I was no longer equipped as a professional to deal with the new technologies that were set to disrupt the mobility sector,” says da Silva Branco Magalhães. “It became clear to me that data and AI were the driving forces behind new products and services such as autonomous vehicles, on-demand transport, or mobility as a service, but I didn’t really understand how data was being used to achieve these outcomes, so I needed to improve my knowledge.”

      July 2023 MicroMasters Program Joint Completion Celebration for SCM, DEDP, PoM, SDS, and FinVideo: MIT Open Learning

      The MicroMasters in Statistics and Data Science was developed by the MIT Institute for Data, Systems, and Society and MITx. Credential holders are required to complete four courses equivalent to graduate-level courses in statistics and data science at MIT and a capstone exam comprising four two-hour proctored exams.

      “The content is world-class,” da Silva Branco Magalhães says of the program. “Even the most complex concepts were explained in a very intuitive way. The exercises and the capstone exam are challenging and stimulating — and MIT-level — which makes this credential highly valuable in the market.”

      Da Silva Branco Magalhães also found the discussion forum very useful, and valued conversations with his colleagues, noting that many of these discussions later continued after completion of the program.

      Gaining analysis and leadership skills

      Now in the Northwestern pathway program, da Silva Branco Magalhães finds that the MicroMasters in Statistics and Data Science program prepared him well for this next step in his studies. The nine-course, accelerated, online master’s program is designed to offer the same depth and rigor of Northwestern’s 12-course MS in Data Science program, aiming to help students build essential analysis and leadership skills that can be directly implemented into the professional realm. Students learn how to make reliable predictions using traditional statistics and machine learning methods.

      Da Silva Branco Magalhães says he has appreciated the remote nature of the Northwestern program, as he started it in France and then completed the first three courses in Australia. He also values the high number of elective courses, allowing students to design the master’s program according to personal preferences and interests.

      “I want to be prepared to meet the challenges and seize the opportunities that AI and data science technologies will bring to the professional realm,” he says. “With this credential, there are no limits to what you can achieve in the field of data science.” More

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      Forging climate connections across the Institute

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      Climate change is the ultimate cross-cutting issue: Not limited to any one discipline, it ranges across science, technology, policy, culture, human behavior, and well beyond. The response to it likewise requires an all-of-MIT effort.

      Now, to strengthen such an effort, a new grant program spearheaded by the Climate Nucleus, the faculty committee charged with the oversight and implementation of Fast Forward: MIT’s Climate Action Plan for the Decade, aims to build up MIT’s climate leadership capacity while also supporting innovative scholarship on diverse climate-related topics and forging new connections across the Institute.

      Called the Fast Forward Faculty Fund (F^4 for short), the program has named its first cohort of six faculty members after issuing its inaugural call for proposals in April 2023. The cohort will come together throughout the year for climate leadership development programming and networking. The program provides financial support for graduate students who will work with the faculty members on the projects — the students will also participate in leadership-building activities — as well as $50,000 in flexible, discretionary funding to be used to support related activities. 

      “Climate change is a crisis that truly touches every single person on the planet,” says Noelle Selin, co-chair of the nucleus and interim director of the Institute for Data, Systems, and Society. “It’s therefore essential that we build capacity for every member of the MIT community to make sense of the problem and help address it. Through the Fast Forward Faculty Fund, our aim is to have a cohort of climate ambassadors who can embed climate everywhere at the Institute.”

      F^4 supports both faculty who would like to begin doing climate-related work, as well as faculty members who are interested in deepening their work on climate. The program has the core goal of developing cohorts of F^4 faculty and graduate students who, in addition to conducting their own research, will become climate leaders at MIT, proactively looking for ways to forge new climate connections across schools, departments, and disciplines.

      One of the projects, “Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies,” led by Professor Siqi Zheng of the MIT Center for Real Estate in collaboration with colleagues from the MIT Sloan School of Management, focuses on the roughly 40 percent of carbon dioxide emissions that come from the buildings and real estate sector. Zheng notes that this sector has been slow to respond to climate change, but says that is starting to change, thanks in part to the rising awareness of climate risks and new local regulations aimed at reducing emissions from buildings.

      Using a data-driven approach, the project seeks to understand the efficient and equitable market incentives, technology solutions, and public policies that are most effective at transforming the real estate industry. Johnattan Ontiveros, a graduate student in the Technology and Policy Program, is working with Zheng on the project.

      “We were thrilled at the incredible response we received from the MIT faculty to our call for proposals, which speaks volumes about the depth and breadth of interest in climate at MIT,” says Anne White, nucleus co-chair and vice provost and associate vice president for research. “This program makes good on key commitments of the Fast Forward plan, supporting cutting-edge new work by faculty and graduate students while helping to deepen the bench of climate leaders at MIT.”

      During the 2023-24 academic year, the F^4 faculty and graduate student cohorts will come together to discuss their projects, explore opportunities for collaboration, participate in climate leadership development, and think proactively about how to deepen interdisciplinary connections among MIT community members interested in climate change.

      The six inaugural F^4 awardees are:

      Professor Tristan Brown, History Section: Humanistic Approaches to the Climate Crisis  

      With this project, Brown aims to create a new community of practice around narrative-centric approaches to environmental and climate issues. Part of a broader humanities initiative at MIT, it brings together a global working group of interdisciplinary scholars, including Serguei Saavedra (Department of Civil and Environmental Engineering) and Or Porath (Tel Aviv University; Religion), collectively focused on examining the historical and present links between sacred places and biodiversity for the purposes of helping governments and nongovernmental organizations formulate better sustainability goals. Boyd Ruamcharoen, a PhD student in the History, Anthropology, and Science, Technology, and Society (HASTS) program, will work with Brown on this project.

      Professor Kerri Cahoy, departments of Aeronautics and Astronautics and Earth, Atmospheric, and Planetary Sciences (AeroAstro): Onboard Autonomous AI-driven Satellite Sensor Fusion for Coastal Region Monitoring

      The motivation for this project is the need for much better data collection from satellites, where technology can be “20 years behind,” says Cahoy. As part of this project, Cahoy will pursue research in the area of autonomous artificial intelligence-enabled rapid sensor fusion (which combines data from different sensors, such as radar and cameras) onboard satellites to improve understanding of the impacts of climate change, specifically sea-level rise and hurricanes and flooding in coastal regions. Graduate students Madeline Anderson, a PhD student in electrical engineering and computer science (EECS), and Mary Dahl, a PhD student in AeroAstro, will work with Cahoy on this project.

      Professor Priya Donti, Department of Electrical Engineering and Computer Science: Robust Reinforcement Learning for High-Renewables Power Grids 

      With renewables like wind and solar making up a growing share of electricity generation on power grids, Donti’s project focuses on improving control methods for these distributed sources of electricity. The research will aim to create a realistic representation of the characteristics of power grid operations, and eventually inform scalable operational improvements in power systems. It will “give power systems operators faith that, OK, this conceptually is good, but it also actually works on this grid,” says Donti. PhD candidate Ana Rivera from EECS is the F^4 graduate student on the project.

      Professor Jason Jackson, Department of Urban Studies and Planning (DUSP): Political Economy of the Climate Crisis: Institutions, Power and Global Governance

      This project takes a political economy approach to the climate crisis, offering a distinct lens to examine, first, the political governance challenge of mobilizing climate action and designing new institutional mechanisms to address the global and intergenerational distributional aspects of climate change; second, the economic challenge of devising new institutional approaches to equitably finance climate action; and third, the cultural challenge — and opportunity — of empowering an adaptive socio-cultural ecology through traditional knowledge and local-level social networks to achieve environmental resilience. Graduate students Chen Chu and Mrinalini Penumaka, both PhD students in DUSP, are working with Jackson on the project.

      Professor Haruko Wainwright, departments of Nuclear Science and Engineering (NSE) and Civil and Environmental Engineering: Low-cost Environmental Monitoring Network Technologies in Rural Communities for Addressing Climate Justice 

      This project will establish a community-based climate and environmental monitoring network in addition to a data visualization and analysis infrastructure in rural marginalized communities to better understand and address climate justice issues. The project team plans to work with rural communities in Alaska to install low-cost air and water quality, weather, and soil sensors. Graduate students Kay Whiteaker, an MS candidate in NSE, and Amandeep Singh, and MS candidate in System Design and Management at Sloan, are working with Wainwright on the project, as is David McGee, professor in earth, atmospheric, and planetary sciences.

      Professor Siqi Zheng, MIT Center for Real Estate and DUSP: Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies 

      See the text above for the details on this project. More

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      J-PAL North America and Results for America announce 18 collaborations with state and local governments

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      J-PAL North America and Results for America have announced 18 new partnerships with state and local governments across the country through their Leveraging Evidence and Evaluation for Equitable Recovery (LEVER) programming, which launched in April of this year. 

      As state and local leaders leverage federal relief funding to invest in their communities, J-PAL North America and Results for America are providing in-depth support to agencies in using data, evaluation, and evidence to advance effective and equitable government programming for generations to come. The 18 new collaborators span the contiguous United States and represent a wide range of pressing and innovative uses of federal Covid-19 recovery funding.

      These partnerships are a key component of the LEVER program, run by J-PAL North America — a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) — and Results for America — a nonprofit organization that helps government agencies harness the power of evidence and data. Through 2024, LEVER will continue to provide a suite of resources, training, and evaluation design services to prepare state and local government agencies to rigorously evaluate their own programs and to harness existing evidence in developing programs and policies using federal recovery dollars.

      J-PAL North America is working with four leading government agencies following a call for proposals to the LEVER Evaluation Incubator in June. These agencies will work with J-PAL staff to design randomized evaluations to understand the causal impact of important programs that contribute to their jurisdictions’ recovery from Covid-19.

      Connecticut’s Medicaid office, operating out of the state’s Department of Social Services, is working to improve vaccine access and awareness among youth. “Connecticut Medicaid is thrilled to work with J-PAL North America. The technical expertise and training that we receive will expand our knowledge during ‘testing and learning’ interventions that improve the health of our members,” says Gui Woolston, the director of Medicaid and Division of Health Services. 

      Athens-Clarke County Unified Government is invested in evaluating programming for youth development and violence prevention implemented by the Boys and Girls Club of Athens. Their goal is “to measure and transparently communicate program impact,” explains Paige Seago, the data and outcomes coordinator for the American Rescue Plan Act. “The ability to continually iterate and tailor programs to better meet community goals is crucial to long-term success.”

      The County of San Diego’s newly formed Office of Evaluation, Performance, and Analytics is evaluating a pilot program providing rental subsidies for older adults. “Randomized evaluation can help us understand if rent subsidies will help prevent seniors from becoming homeless and will give us useful information about how to move forward,” says Chief Evaluation Officer Ricardo Basurto-Dávila. 

      In King County, Washington, the Executive Climate Office is planning to evaluate efforts to increase equitable access to household energy efficiency programs. “Because of J-PAL’s support, we have confidence that we can reduce climate impacts and extend home electrification benefits to lower-income homeowners in King County — homeowners who otherwise may not have the ability to participate in the clean energy transition,” says King County Climate Director Marissa Aho.

      Fourteen additional state and local agencies are working with Results for America as part of the LEVER Training Sprint. Together, they will develop policies that catalyze sustainable evidence building within government. 

      Jurisdictions selected for the Training Sprint represent government leaders at the city, county, and state levels — all of whom are committed to creating an evaluation framework for policy that will prioritize evidence-based decision-making across the country. Over the course of 10 weeks, with access to tools and coaching, each team will develop an internal implementation policy by embedding key evaluation and evidence practices into their jurisdiction’s decision-making processes. Participants will finish the Training Sprint with a robust decision-making framework that translates their LEVER implementation policies into actionable planning guidance. 

      Government leaders will utilize the LEVER Training Sprint to build a culture of data and evidence focused on leveraging evaluation policies to invest in delivering tangible results for their residents. About their participation in the LEVER Training Sprint, Dana Williams from Denver, Colorado says, “Impact evaluation is such an integral piece to understanding the past, present, and future. I’m excited to participate in the LEVER Training Sprint to better inform and drive evidence-based programming in Denver.”

      The Training Sprint is a part of a growing movement to ground government innovation in data and evidence. Kermina Hanna from the State of New Jersey notes, “It’s vital that we cement a data-driven commitment to equity in government operations, and I’m really excited for this opportunity to develop a national network of colleagues in government who share this passion and dedication to responsive public service.”

      Jurisdictions selected for the Training Sprint are: 

      Boston, Massachusetts;
      Carlsbad, California;
      Connecticut;
      Dallas, Texas;
      Denver City/County, Colorado;
      Fort Collins, Colorado;
      Guilford County, North Carolina;
      King County, Washington;
      Long Beach, California;
      Los Angeles, California;
      New Jersey;
      New Mexico;
      Pittsburgh, Pennsylvania; and
      Washington County, Oregon.
      Those interested in learning more can fill out the LEVER intake form. Please direct any questions about the Evaluation Incubator to Louise Geraghty and questions about the Training Sprint to Chelsea Powell. More

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      Fast-tracking fusion energy’s arrival with AI and accessibility

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      As the impacts of climate change continue to grow, so does interest in fusion’s potential as a clean energy source. While fusion reactions have been studied in laboratories since the 1930s, there are still many critical questions scientists must answer to make fusion power a reality, and time is of the essence. As part of their strategy to accelerate fusion energy’s arrival and reach carbon neutrality by 2050, the U.S. Department of Energy (DoE) has announced new funding for a project led by researchers at MIT’s Plasma Science and Fusion Center (PSFC) and four collaborating institutions.

      Cristina Rea, a research scientist and group leader at the PSFC, will serve as the primary investigator for the newly funded three-year collaboration to pilot the integration of fusion data into a system that can be read by AI-powered tools. The PSFC, together with scientists from William & Mary, the University of Wisconsin at Madison, Auburn University, and the nonprofit HDF Group, plan to create a holistic fusion data platform, the elements of which could offer unprecedented access for researchers, especially underrepresented students. The project aims to encourage diverse participation in fusion and data science, both in academia and the workforce, through outreach programs led by the group’s co-investigators, of whom four out of five are women. 

      The DoE’s award, part of a $29 million funding package for seven projects across 19 institutions, will support the group’s efforts to distribute data produced by fusion devices like the PSFC’s Alcator C-Mod, a donut-shaped “tokamak” that utilized powerful magnets to control and confine fusion reactions. Alcator C-Mod operated from 1991 to 2016 and its data are still being studied, thanks in part to the PSFC’s commitment to the free exchange of knowledge.

      Currently, there are nearly 50 public experimental magnetic confinement-type fusion devices; however, both historical and current data from these devices can be difficult to access. Some fusion databases require signing user agreements, and not all data are catalogued and organized the same way. Moreover, it can be difficult to leverage machine learning, a class of AI tools, for data analysis and to enable scientific discovery without time-consuming data reorganization. The result is fewer scientists working on fusion, greater barriers to discovery, and a bottleneck in harnessing AI to accelerate progress.

      The project’s proposed data platform addresses technical barriers by being FAIR — Findable, Interoperable, Accessible, Reusable — and by adhering to UNESCO’s Open Science (OS) recommendations to improve the transparency and inclusivity of science; all of the researchers’ deliverables will adhere to FAIR and OS principles, as required by the DoE. The platform’s databases will be built using MDSplusML, an upgraded version of the MDSplus open-source software developed by PSFC researchers in the 1980s to catalogue the results of Alcator C-Mod’s experiments. Today, nearly 40 fusion research institutes use MDSplus to store and provide external access to their fusion data. The release of MDSplusML aims to continue that legacy of open collaboration.

      The researchers intend to address barriers to participation for women and disadvantaged groups not only by improving general access to fusion data, but also through a subsidized summer school that will focus on topics at the intersection of fusion and machine learning, which will be held at William & Mary for the next three years.

      Of the importance of their research, Rea says, “This project is about responding to the fusion community’s needs and setting ourselves up for success. Scientific advancements in fusion are enabled via multidisciplinary collaboration and cross-pollination, so accessibility is absolutely essential. I think we all understand now that diverse communities have more diverse ideas, and they allow faster problem-solving.”

      The collaboration’s work also aligns with vital areas of research identified in the International Atomic Energy Agency’s “AI for Fusion” Coordinated Research Project (CRP). Rea was selected as the technical coordinator for the IAEA’s CRP emphasizing community engagement and knowledge access to accelerate fusion research and development. In a letter of support written for the group’s proposed project, the IAEA stated that, “the work [the researchers] will carry out […] will be beneficial not only to our CRP but also to the international fusion community in large.”

      PSFC Director and Hitachi America Professor of Engineering Dennis Whyte adds, “I am thrilled to see PSFC and our collaborators be at the forefront of applying new AI tools while simultaneously encouraging and enabling extraction of critical data from our experiments.”

      “Having the opportunity to lead such an important project is extremely meaningful, and I feel a responsibility to show that women are leaders in STEM,” says Rea. “We have an incredible team, strongly motivated to improve our fusion ecosystem and to contribute to making fusion energy a reality.” More

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      Supporting sustainability, digital health, and the future of work

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      The MIT and Accenture Convergence Initiative for Industry and Technology has selected three new research projects that will receive support from the initiative. The research projects aim to accelerate progress in meeting complex societal needs through new business convergence insights in technology and innovation.

      Established in MIT’s School of Engineering and now in its third year, the MIT and Accenture Convergence Initiative is furthering its mission to bring together technological experts from across business and academia to share insights and learn from one another. Recently, Thomas W. Malone, the Patrick J. McGovern (1959) Professor of Management, joined the initiative as its first-ever faculty lead. The research projects relate to three of the initiative’s key focus areas: sustainability, digital health, and the future of work.

      “The solutions these research teams are developing have the potential to have tremendous impact,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “They embody the initiative’s focus on advancing data-driven research that addresses technology and industry convergence.”

      “The convergence of science and technology driven by advancements in generative AI, digital twins, quantum computing, and other technologies makes this an especially exciting time for Accenture and MIT to be undertaking this joint research,” says Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences. “Our three new research projects focusing on sustainability, digital health, and the future of work have the potential to help guide and shape future innovations that will benefit the way we work and live.”

      The MIT and Accenture Convergence Initiative charter project researchers are described below.

      Accelerating the journey to net zero with industrial clusters

      Jessika Trancik is a professor at the Institute for Data, Systems, and Society (IDSS). Trancik’s research examines the dynamic costs, performance, and environmental impacts of energy systems to inform climate policy and accelerate beneficial and equitable technology innovation. Trancik’s project aims to identify how industrial clusters can enable companies to derive greater value from decarbonization, potentially making companies more willing to invest in the clean energy transition.

      To meet the ambitious climate goals that have been set by countries around the world, rising greenhouse gas emissions trends must be rapidly reversed. Industrial clusters — geographically co-located or otherwise-aligned groups of companies representing one or more industries — account for a significant portion of greenhouse gas emissions globally. With major energy consumers “clustered” in proximity, industrial clusters provide a potential platform to scale low-carbon solutions by enabling the aggregation of demand and the coordinated investment in physical energy supply infrastructure.

      In addition to Trancik, the research team working on this project will include Aliza Khurram, a postdoc in IDSS; Micah Ziegler, an IDSS research scientist; Melissa Stark, global energy transition services lead at Accenture; Laura Sanderfer, strategy consulting manager at Accenture; and Maria De Miguel, strategy senior analyst at Accenture.

      Eliminating childhood obesity

      Anette “Peko” Hosoi is the Neil and Jane Pappalardo Professor of Mechanical Engineering. A common theme in her work is the fundamental study of shape, kinematic, and rheological optimization of biological systems with applications to the emergent field of soft robotics. Her project will use both data from existing studies and synthetic data to create a return-on-investment (ROI) calculator for childhood obesity interventions so that companies can identify earlier returns on their investment beyond reduced health-care costs.

      Childhood obesity is too prevalent to be solved by a single company, industry, drug, application, or program. In addition to the physical and emotional impact on children, society bears a cost through excess health care spending, lost workforce productivity, poor school performance, and increased family trauma. Meaningful solutions require multiple organizations, representing different parts of society, working together with a common understanding of the problem, the economic benefits, and the return on investment. ROI is particularly difficult to defend for any single organization because investment and return can be separated by many years and involve asymmetric investments, returns, and allocation of risk. Hosoi’s project will consider the incentives for a particular entity to invest in programs in order to reduce childhood obesity.

      Hosoi will be joined by graduate students Pragya Neupane and Rachael Kha, both of IDSS, as well a team from Accenture that includes Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences; Kaveh Safavi, senior managing director in Accenture Health Industry; and Elizabeth Naik, global health and public service research lead.

      Generating innovative organizational configurations and algorithms for dealing with the problem of post-pandemic employment

      Thomas Malone is the Patrick J. McGovern (1959) Professor of Management at the MIT Sloan School of Management and the founding director of the MIT Center for Collective Intelligence. His research focuses on how new organizations can be designed to take advantage of the possibilities provided by information technology. Malone will be joined in this project by John Horton, the Richard S. Leghorn (1939) Career Development Professor at the MIT Sloan School of Management, whose research focuses on the intersection of labor economics, market design, and information systems. Malone and Horton’s project will look to reshape the future of work with the help of lessons learned in the wake of the pandemic.

      The Covid-19 pandemic has been a major disrupter of work and employment, and it is not at all obvious how governments, businesses, and other organizations should manage the transition to a desirable state of employment as the pandemic recedes. Using natural language processing algorithms such as GPT-4, this project will look to identify new ways that companies can use AI to better match applicants to necessary jobs, create new types of jobs, assess skill training needed, and identify interventions to help include women and other groups whose employment was disproportionately affected by the pandemic.

      In addition to Malone and Horton, the research team will include Rob Laubacher, associate director and research scientist at the MIT Center for Collective Intelligence, and Kathleen Kennedy, executive director at the MIT Center for Collective Intelligence and senior director at MIT Horizon. The team will also include Nitu Nivedita, managing director of artificial intelligence at Accenture, and Thomas Hancock, data science senior manager at Accenture. More

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      in Data Management & Statistics

      Artificial intelligence for augmentation and productivity

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      The MIT Stephen A. Schwarzman College of Computing has awarded seed grants to seven projects that are exploring how artificial intelligence and human-computer interaction can be leveraged to enhance modern work spaces to achieve better management and higher productivity.

      Funded by Andrew W. Houston ’05 and Dropbox Inc., the projects are intended to be interdisciplinary and bring together researchers from computing, social sciences, and management.

      The seed grants can enable the project teams to conduct research that leads to bigger endeavors in this rapidly evolving area, as well as build community around questions related to AI-augmented management.

      The seven selected projects and research leads include:

      “LLMex: Implementing Vannevar Bush’s Vision of the Memex Using Large Language Models,” led by Patti Maes of the Media Lab and David Karger of the Department of Electrical Engineering and Computer Science (EECS) and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Inspired by Vannevar Bush’s Memex, this project proposes to design, implement, and test the concept of memory prosthetics using large language models (LLMs). The AI-based system will intelligently help an individual keep track of vast amounts of information, accelerate productivity, and reduce errors by automatically recording their work actions and meetings, supporting retrieval based on metadata and vague descriptions, and suggesting relevant, personalized information proactively based on the user’s current focus and context.

      “Using AI Agents to Simulate Social Scenarios,” led by John Horton of the MIT Sloan School of Management and Jacob Andreas of EECS and CSAIL. This project imagines the ability to easily simulate policies, organizational arrangements, and communication tools with AI agents before implementation. Tapping into the capabilities of modern LLMs to serve as a computational model of humans makes this vision of social simulation more realistic, and potentially more predictive.

      “Human Expertise in the Age of AI: Can We Have Our Cake and Eat it Too?” led by Manish Raghavan of MIT Sloan and EECS, and Devavrat Shah of EECS and the Laboratory for Information and Decision Systems. Progress in machine learning, AI, and in algorithmic decision aids has raised the prospect that algorithms may complement human decision-making in a wide variety of settings. Rather than replacing human professionals, this project sees a future where AI and algorithmic decision aids play a role that is complementary to human expertise.

      “Implementing Generative AI in U.S. Hospitals,” led by Julie Shah of the Department of Aeronautics and Astronautics and CSAIL, Retsef Levi of MIT Sloan and the Operations Research Center, Kate Kellog of MIT Sloan, and Ben Armstrong of the Industrial Performance Center. In recent years, studies have linked a rise in burnout from doctors and nurses in the United States with increased administrative burdens associated with electronic health records and other technologies. This project aims to develop a holistic framework to study how generative AI technologies can both increase productivity for organizations and improve job quality for workers in health care settings.

      “Generative AI Augmented Software Tools to Democratize Programming,” led by Harold Abelson of EECS and CSAIL, Cynthia Breazeal of the Media Lab, and Eric Klopfer of the Comparative Media Studies/Writing. Progress in generative AI over the past year is fomenting an upheaval in assumptions about future careers in software and deprecating the role of coding. This project will stimulate a similar transformation in computing education for those who have no prior technical training by creating a software tool that could eliminate much of the need for learners to deal with code when creating applications.

      “Acquiring Expertise and Societal Productivity in a World of Artificial Intelligence,” led by David Atkin and Martin Beraja of the Department of Economics, and Danielle Li of MIT Sloan. Generative AI is thought to augment the capabilities of workers performing cognitive tasks. This project seeks to better understand how the arrival of AI technologies may impact skill acquisition and productivity, and to explore complementary policy interventions that will allow society to maximize the gains from such technologies.

      “AI Augmented Onboarding and Support,” led by Tim Kraska of EECS and CSAIL, and Christoph Paus of the Department of Physics. While LLMs have made enormous leaps forward in recent years and are poised to fundamentally change the way students and professionals learn about new tools and systems, there is often a steep learning curve which people have to climb in order to make full use of the resource. To help mitigate the issue, this project proposes the development of new LLM-powered onboarding and support systems that will positively impact the way support teams operate and improve the user experience. More