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    MIT ARCLab announces winners of inaugural Prize for AI Innovation in Space

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    Satellite density in Earth’s orbit has increased exponentially in recent years, with lower costs of small satellites allowing governments, researchers, and private companies to launch and operate some 2,877 satellites into orbit in 2023 alone. This includes increased geostationary Earth orbit (GEO) satellite activity, which brings technologies with global-scale impact, from broadband internet to climate surveillance. Along with the manifold benefits of these satellite-enabled technologies, however, come increased safety and security risks, as well as environmental concerns. More accurate and efficient methods of monitoring and modeling satellite behavior are urgently needed to prevent collisions and other disasters.To address this challenge, the MIT Astrodynamics, Space Robotic, and Controls Laboratory (ARCLab) launched the MIT ARCLab Prize for AI Innovation in Space: a first-of-its-kind competition asking contestants to harness AI to characterize satellites’ patterns of life (PoLs) — the long-term behavioral narrative of a satellite in orbit — using purely passively collected information. Following the call for participants last fall, 126 teams used machine learning to create algorithms to label and time-stamp the behavioral modes of GEO satellites over a six-month period, competing for accuracy and efficiency.With support from the U.S. Department of the Air Force-MIT AI Accelerator, the challenge offers a total of $25,000. A team of judges from ARCLab and MIT Lincoln Laboratory evaluated the submissions based on clarity, novelty, technical depth, and reproducibility, assigning each entry a score out of 100 points. Now the judges have announced the winners and runners-up:First prize: David Baldsiefen — Team Hawaii2024With a winning score of 96, Baldsiefen will be awarded $10,000 and is invited to join the ARCLab team in presenting at a poster session at the Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference in Hawaii this fall. One evaluator noted, “Clear and concise report, with very good ideas such as the label encoding of the localizer. Decisions on the architectures and the feature engineering are well reasoned. The code provided is also well documented and structured, allowing an easy reproducibility of the experimentation.”Second prize: Binh Tran, Christopher Yeung, Kurtis Johnson, Nathan Metzger — Team Millennial-IUPWith a score of 94.2, Y, Millennial-IUP will be awarded $5,000 and will also join the ARCLab team at the AMOS conference. One evaluator said, “The models chosen were sensible and justified, they made impressive efforts in efficiency gains… They used physics to inform their models and this appeared to be reproducible. Overall it was an easy to follow, concise report without much jargon.”Third Prize: Isaac Haik and Francois Porcher — Team QR_IsWith a score of 94, Haik and Porcher will share the third prize of $3,000 and will also be invited to the AMOS conference with the ARCLab team. One evaluator noted, “This informative and interesting report describes the combination of ML and signal processing techniques in a compelling way, assisted by informative plots, tables, and sequence diagrams. The author identifies and describes a modular approach to class detection and their assessment of feature utility, which they correctly identify is not evenly useful across classes… Any lack of mission expertise is made up for by a clear and detailed discussion of the benefits and pitfalls of the methods they used and discussion of what they learned.”The fourth- through seventh-place scoring teams will each receive $1,000 and a certificate of excellence.“The goal of this competition was to foster an interdisciplinary approach to problem-solving in the space domain by inviting AI development experts to apply their skills in this new context of orbital capacity. And all of our winning teams really delivered — they brought technical skill, novel approaches, and expertise to a very impressive round of submissions.” says Professor Richard Linares, who heads ARCLab.Active modeling with passive dataThroughout a GEO satellite’s time in orbit, operators issue commands to place them in various behavioral modes—station-keeping, longitudinal shifts, end-of-life behaviors, and so on. Satellite Patterns of Life (PoLs) describe on-orbit behavior composed of sequences of both natural and non-natural behavior modes.ARCLab has developed a groundbreaking benchmarking tool for geosynchronous satellite pattern-of-life characterization and created the Satellite Pattern-of-Life Identification Dataset (SPLID), comprising real and synthetic space object data. The challenge participants used this tool to create algorithms that use AI to map out the on-orbit behaviors of a satellite.The goal of the MIT ARCLab Prize for AI Innovation in Space is to encourage technologists and enthusiasts to bring innovation and new skills sets to well-established challenges in aerospace. The team aims to hold the competition in 2025 and 2026 to explore other topics and invite experts in AI to apply their skills to new challenges.  More

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      Study finds health risks in switching ships from diesel to ammonia fuel

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      As container ships the size of city blocks cross the oceans to deliver cargo, their huge diesel engines emit large quantities of air pollutants that drive climate change and have human health impacts. It has been estimated that maritime shipping accounts for almost 3 percent of global carbon dioxide emissions and the industry’s negative impacts on air quality cause about 100,000 premature deaths each year.Decarbonizing shipping to reduce these detrimental effects is a goal of the International Maritime Organization, a U.N. agency that regulates maritime transport. One potential solution is switching the global fleet from fossil fuels to sustainable fuels such as ammonia, which could be nearly carbon-free when considering its production and use.But in a new study, an interdisciplinary team of researchers from MIT and elsewhere caution that burning ammonia for maritime fuel could worsen air quality further and lead to devastating public health impacts, unless it is adopted alongside strengthened emissions regulations.Ammonia combustion generates nitrous oxide (N2O), a greenhouse gas that is about 300 times more potent than carbon dioxide. It also emits nitrogen in the form of nitrogen oxides (NO and NO2, referred to as NOx), and unburnt ammonia may slip out, which eventually forms fine particulate matter in the atmosphere. These tiny particles can be inhaled deep into the lungs, causing health problems like heart attacks, strokes, and asthma.The new study indicates that, under current legislation, switching the global fleet to ammonia fuel could cause up to about 600,000 additional premature deaths each year. However, with stronger regulations and cleaner engine technology, the switch could lead to about 66,000 fewer premature deaths than currently caused by maritime shipping emissions, with far less impact on global warming.“Not all climate solutions are created equal. There is almost always some price to pay. We have to take a more holistic approach and consider all the costs and benefits of different climate solutions, rather than just their potential to decarbonize,” says Anthony Wong, a postdoc in the MIT Center for Global Change Science and lead author of the study.His co-authors include Noelle Selin, an MIT professor in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences (EAPS); Sebastian Eastham, a former principal research scientist who is now a senior lecturer at Imperial College London; Christine Mounaïm-Rouselle, a professor at the University of Orléans in France; Yiqi Zhang, a researcher at the Hong Kong University of Science and Technology; and Florian Allroggen, a research scientist in the MIT Department of Aeronautics and Astronautics. The research appears this week in Environmental Research Letters.Greener, cleaner ammoniaTraditionally, ammonia is made by stripping hydrogen from natural gas and then combining it with nitrogen at extremely high temperatures. This process is often associated with a large carbon footprint. The maritime shipping industry is betting on the development of “green ammonia,” which is produced by using renewable energy to make hydrogen via electrolysis and to generate heat.“In theory, if you are burning green ammonia in a ship engine, the carbon emissions are almost zero,” Wong says.But even the greenest ammonia generates nitrous oxide (N2O), nitrogen oxides (NOx) when combusted, and some of the ammonia may slip out, unburnt. This nitrous oxide would escape into the atmosphere, where the greenhouse gas would remain for more than 100 years. At the same time, the nitrogen emitted as NOx and ammonia would fall to Earth, damaging fragile ecosystems. As these emissions are digested by bacteria, additional N2O  is produced.NOx and ammonia also mix with gases in the air to form fine particulate matter. A primary contributor to air pollution, fine particulate matter kills an estimated 4 million people each year.“Saying that ammonia is a ‘clean’ fuel is a bit of an overstretch. Just because it is carbon-free doesn’t necessarily mean it is clean and good for public health,” Wong says.A multifaceted modelThe researchers wanted to paint the whole picture, capturing the environmental and public health impacts of switching the global fleet to ammonia fuel. To do so, they designed scenarios to measure how pollutant impacts change under certain technology and policy assumptions.From a technological point of view, they considered two ship engines. The first burns pure ammonia, which generates higher levels of unburnt ammonia but emits fewer nitrogen oxides. The second engine technology involves mixing ammonia with hydrogen to improve combustion and optimize the performance of a catalytic converter, which controls both nitrogen oxides and unburnt ammonia pollution.They also considered three policy scenarios: current regulations, which only limit NOx emissions in some parts of the world; a scenario that adds ammonia emission limits over North America and Western Europe; and a scenario that adds global limits on ammonia and NOx emissions.The researchers used a ship track model to calculate how pollutant emissions change under each scenario and then fed the results into an air quality model. The air quality model calculates the impact of ship emissions on particulate matter and ozone pollution. Finally, they estimated the effects on global public health.One of the biggest challenges came from a lack of real-world data, since no ammonia-powered ships are yet sailing the seas. Instead, the researchers relied on experimental ammonia combustion data from collaborators to build their model.“We had to come up with some clever ways to make that data useful and informative to both the technology and regulatory situations,” he says.A range of outcomesIn the end, they found that with no new regulations and ship engines that burn pure ammonia, switching the entire fleet would cause 681,000 additional premature deaths each year.“While a scenario with no new regulations is not very realistic, it serves as a good warning of how dangerous ammonia emissions could be. And unlike NOx, ammonia emissions from shipping are currently unregulated,” Wong says.However, even without new regulations, using cleaner engine technology would cut the number of premature deaths down to about 80,000, which is about 20,000 fewer than are currently attributed to maritime shipping emissions. With stronger global regulations and cleaner engine technology, the number of people killed by air pollution from shipping could be reduced by about 66,000.“The results of this study show the importance of developing policies alongside new technologies,” Selin says. “There is a potential for ammonia in shipping to be beneficial for both climate and air quality, but that requires that regulations be designed to address the entire range of potential impacts, including both climate and air quality.”Ammonia’s air quality impacts would not be felt uniformly across the globe, and addressing them fully would require coordinated strategies across very different contexts. Most premature deaths would occur in East Asia, since air quality regulations are less stringent in this region. Higher levels of existing air pollution cause the formation of more particulate matter from ammonia emissions. In addition, shipping volume over East Asia is far greater than elsewhere on Earth, compounding these negative effects.In the future, the researchers want to continue refining their analysis. They hope to use these findings as a starting point to urge the marine industry to share engine data they can use to better evaluate air quality and climate impacts. They also hope to inform policymakers about the importance and urgency of updating shipping emission regulations.This research was funded by the MIT Climate and Sustainability Consortium. More

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      A technique for more effective multipurpose robots

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      Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.Existing robotic datasets vary widely in modality — some include color images while others are composed of tactile imprints, for instance. Data could also be collected in different domains, like simulation or human demos. And each dataset may capture a unique task and environment.It is difficult to efficiently incorporate data from so many sources in one machine-learning model, so many methods use just one type of data to train a robot. But robots trained this way, with a relatively small amount of task-specific data, are often unable to perform new tasks in unfamiliar environments.In an effort to train better multipurpose robots, MIT researchers developed a technique to combine multiple sources of data across domains, modalities, and tasks using a type of generative AI known as diffusion models.They train a separate diffusion model to learn a strategy, or policy, for completing one task using one specific dataset. Then they combine the policies learned by the diffusion models into a general policy that enables a robot to perform multiple tasks in various settings.In simulations and real-world experiments, this training approach enabled a robot to perform multiple tool-use tasks and adapt to new tasks it did not see during training. The method, known as Policy Composition (PoCo), led to a 20 percent improvement in task performance when compared to baseline techniques.“Addressing heterogeneity in robotic datasets is like a chicken-egg problem. If we want to use a lot of data to train general robot policies, then we first need deployable robots to get all this data. I think that leveraging all the heterogeneous data available, similar to what researchers have done with ChatGPT, is an important step for the robotics field,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on PoCo.     Wang’s coauthors include Jialiang Zhao, a mechanical engineering graduate student; Yilun Du, an EECS graduate student; Edward Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of CSAIL. The research will be presented at the Robotics: Science and Systems Conference.Combining disparate datasetsA robotic policy is a machine-learning model that takes inputs and uses them to perform an action. One way to think about a policy is as a strategy. In the case of a robotic arm, that strategy might be a trajectory, or a series of poses that move the arm so it picks up a hammer and uses it to pound a nail.Datasets used to learn robotic policies are typically small and focused on one particular task and environment, like packing items into boxes in a warehouse.“Every single robotic warehouse is generating terabytes of data, but it only belongs to that specific robot installation working on those packages. It is not ideal if you want to use all of these data to train a general machine,” Wang says.The MIT researchers developed a technique that can take a series of smaller datasets, like those gathered from many robotic warehouses, learn separate policies from each one, and combine the policies in a way that enables a robot to generalize to many tasks.They represent each policy using a type of generative AI model known as a diffusion model. Diffusion models, often used for image generation, learn to create new data samples that resemble samples in a training dataset by iteratively refining their output.But rather than teaching a diffusion model to generate images, the researchers teach it to generate a trajectory for a robot. They do this by adding noise to the trajectories in a training dataset. The diffusion model gradually removes the noise and refines its output into a trajectory.This technique, known as Diffusion Policy, was previously introduced by researchers at MIT, Columbia University, and the Toyota Research Institute. PoCo builds off this Diffusion Policy work. The team trains each diffusion model with a different type of dataset, such as one with human video demonstrations and another gleaned from teleoperation of a robotic arm.Then the researchers perform a weighted combination of the individual policies learned by all the diffusion models, iteratively refining the output so the combined policy satisfies the objectives of each individual policy.Greater than the sum of its parts“One of the benefits of this approach is that we can combine policies to get the best of both worlds. For instance, a policy trained on real-world data might be able to achieve more dexterity, while a policy trained on simulation might be able to achieve more generalization,” Wang says.

      With policy composition, researchers are able to combine datasets from multiple sources so they can teach a robot to effectively use a wide range of tools, like a hammer, screwdriver, or this spatula.Image: Courtesy of the researchers

      Because the policies are trained separately, one could mix and match diffusion policies to achieve better results for a certain task. A user could also add data in a new modality or domain by training an additional Diffusion Policy with that dataset, rather than starting the entire process from scratch.

      The policy composition technique the researchers developed can be used to effectively teach a robot to use tools even when objects are placed around it to try and distract it from its task, as seen here.Image: Courtesy of the researchers

      The researchers tested PoCo in simulation and on real robotic arms that performed a variety of tools tasks, such as using a hammer to pound a nail and flipping an object with a spatula. PoCo led to a 20 percent improvement in task performance compared to baseline methods.“The striking thing was that when we finished tuning and visualized it, we can clearly see that the composed trajectory looks much better than either one of them individually,” Wang says.In the future, the researchers want to apply this technique to long-horizon tasks where a robot would pick up one tool, use it, then switch to another tool. They also want to incorporate larger robotics datasets to improve performance.“We will need all three kinds of data to succeed for robotics: internet data, simulation data, and real robot data. How to combine them effectively will be the million-dollar question. PoCo is a solid step on the right track,” says Jim Fan, senior research scientist at NVIDIA and leader of the AI Agents Initiative, who was not involved with this work.This research is funded, in part, by Amazon, the Singapore Defense Science and Technology Agency, the U.S. National Science Foundation, and the Toyota Research Institute. More

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      Inclusive research for social change

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      Pair a decades-old program dedicated to creating research opportunities for underrepresented minorities and populations with a growing initiative committed to tackling the very issues at the heart of such disparities, and you’ll get a transformative partnership that only MIT can deliver. 

      Since 1986, the MIT Summer Research Program (MSRP) has led an institutional effort to prepare underrepresented students (minorities, women in STEM, or students with low socioeconomic status) for doctoral education by pairing them with MIT labs and research groups. For the past three years, the Initiative on Combatting Systemic Racism (ICSR), a cross-disciplinary research collaboration led by MIT’s Institute for Data, Systems, and Society (IDSS), has joined them in their mission, helping bring the issue full circle by providing MSRP students with the opportunity to use big data and computational tools to create impactful changes toward racial equity.

      “ICSR has further enabled our direct engagement with undergrads, both within and outside of MIT,” says Fotini Christia, the Ford International Professor of the Social Sciences, associate director of IDSS, and co-organizer for the initiative. “We’ve found that this line of research has attracted students interested in examining these topics with the most rigorous methods.”

      The initiative fits well under the IDSS banner, as IDSS research seeks solutions to complex societal issues through a multidisciplinary approach that includes statistics, computation, modeling, social science methodologies, human behavior, and an understanding of complex systems. With the support of faculty and researchers from all five schools and the MIT Schwarzman College of Computing, the objective of ICSR is to work on an array of different societal aspects of systemic racism through a set of verticals including policing, housing, health care, and social media.

      Where passion meets impact

      Grinnell senior Mia Hines has always dreamed of using her love for computer science to support social justice. She has experience working with unhoused people and labor unions, and advocating for Indigenous peoples’ rights. When applying to college, she focused her essay on using technology to help Syrian refugees.

      “As a Black woman, it’s very important to me that we focus on these areas, especially on how we can use technology to help marginalized communities,” Hines says. “And also, how do we stop technology or improve technology that is already hurting marginalized communities?”   

      Through MSRP, Hines was paired with research advisor Ufuoma Ovienmhada, a fourth-year doctoral student in the Department of Aeronautics and Astronautics at MIT. A member of Professor Danielle Wood’s Space Enabled research group at MIT’s Media Lab, Ovienmhada received funding from an ICSR Seed Grant and NASA’s Applied Sciences Program to support her ongoing research measuring environmental injustice and socioeconomic disparities in prison landscapes. 

      “I had been doing satellite remote sensing for environmental challenges and sustainability, starting out looking at coastal ecosystems, when I learned about an issue called ‘prison ecology,’” Ovienmhada explains. “This refers to the intersection of mass incarceration and environmental justice.”

      Ovienmhada’s research uses satellite remote sensing and environmental data to characterize exposures to different environmental hazards such as air pollution, extreme heat, and flooding. “This allows others to use these datasets for real-time advocacy, in addition to creating public awareness,” she says.

      Focused especially on extreme heat, Hines used satellite remote sensing to monitor the fluctuation of temperature to assess the risk being imposed on prisoners, including death, especially in states like Texas, where 75 percent of prisons either don’t have full air conditioning or have none at all.

      “Before this project I had done little to no work with geospatial data, and as a budding data scientist, getting to work with and understanding different types of data and resources is really helpful,” Hines says. “I was also funded and afforded the flexibility to take advantage of IDSS’s Data Science and Machine Learning online course. It was really great to be able to do that and learn even more.”

      Filling the gap

      Much like Hines, Harvey Mudd senior Megan Li was specifically interested in the IDSS-supported MSRP projects. She was drawn to the interdisciplinary approach, and she seeks in her own work to apply computational methods to societal issues and to make computer science more inclusive, considerate, and ethical. 

      Working with Aurora Zhang, a grad student in IDSS’s Social and Engineering Systems PhD program, Li used county-level data on income and housing prices to quantify and visualize how affordability based on income alone varies across the United States. She then expanded the analysis to include assets and debt to determine the most common barriers to home ownership.

      “I spent my day-to-day looking at census data and writing Python scripts that could work with it,” reports Li. “I also reached out to the Census Bureau directly to learn a little bit more about how they did their data collection, and discussed questions related to some of their previous studies and working papers that I had reviewed.” 

      Outside of actual day-to-day research, Li says she learned a lot in conversations with fellow researchers, particularly changing her “skeptical view” of whether or not mortgage lending algorithms would help or hurt home buyers in the approval process. “I think I have a little bit more faith now, which is a good thing.”

      “Harvey Mudd is undergraduate-only, and while professors do run labs here, my specific research areas are not well represented,” Li says. “This opportunity was enormous in that I got the experience I need to see if this research area is actually something that I want to do long term, and I got more mirrors into what I would be doing in grad school from talking to students and getting to know faculty.”

      Closing the loop

      While participating in MSRP offered crucial research experience to Hines, the ICSR projects enabled her to engage in topics she’s passionate about and work that could drive tangible societal change.

      “The experience felt much more concrete because we were working on these very sophisticated projects, in a supportive environment where people were very excited to work with us,” she says.

      A significant benefit for Li was the chance to steer her research in alignment with her own interests. “I was actually given the opportunity to propose my own research idea, versus supporting a graduate student’s work in progress,” she explains. 

      For Ovienmhada, the pairing of the two initiatives solidifies the efforts of MSRP and closes a crucial loop in diversity, equity, and inclusion advocacy. 

      “I’ve participated in a lot of different DEI-related efforts and advocacy and one thing that always comes up is the fact that it’s not just about bringing people in, it’s also about creating an environment and opportunities that align with people’s values,” Ovienmhada says. “Programs like MSRP and ICSR create opportunities for people who want to do work that’s aligned with certain values by providing the needed mentoring and financial support.” More

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      Rewarding excellence in open data

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      The second annual MIT Prize for Open Data, which included a $2,500 cash prize, was recently awarded to 10 individual and group research projects. Presented jointly by the School of Science and the MIT Libraries, the prize highlights the value of open data — research data that is openly accessible and reusable — at the Institute. The prize winners and 12 honorable mention recipients were honored at the Open Data @ MIT event held Oct. 24 at Hayden Library. 

      Conceived by Chris Bourg, director of MIT Libraries, and Rebecca Saxe, associate dean of the School of Science and the John W. Jarve (1978) Professor of Brain and Cognitive Sciences, the prize program was launched in 2022. It recognizes MIT-affiliated researchers who use or share open data, create infrastructure for open data sharing, or theorize about open data. Nominations were solicited from across the Institute, with a focus on trainees: undergraduate and graduate students, postdocs, and research staff. 

      “The prize is explicitly aimed at early-career researchers,” says Bourg. “Supporting and encouraging the next generation of researchers will help ensure that the future of scholarship is characterized by a norm of open sharing.”

      The 2023 awards were presented at a celebratory event held during International Open Access Week. Winners gave five-minute presentations on their projects and the role that open data plays in their research. The program also included remarks from Bourg and Anne White, School of Engineering Distinguished Professor of Engineering, vice provost, and associate vice president for research administration. White reflected on the ways in which MIT has demonstrated its values with the open sharing of research and scholarship and acknowledged the efforts of the honorees and advocates gathered at the event: “Thank you for the active role you’re all playing in building a culture of openness in research,” she said. “It benefits us all.” 

      Winners were chosen from more than 80 nominees, representing all five MIT schools, the MIT Schwarzman College of Computing, and several research centers across the Institute. A committee composed of faculty, staff, and graduate students made the selections:

      Hammaad Adam, graduate student in the Institute for Data, Systems, and Society, accepted on behalf of the team behind Organ Retrieval and Collection of Health Information for Donation (ORCHID), the first ever multi-center dataset dedicated to the organ procurement process. ORCHID provides the first opportunity to quantitatively analyze organ procurement organization decisions and identify operational inefficiencies.
      Adam Atanas, postdoc in the Department of Brain and Cognitive Sciences (BCS), and Jungsoo Kim, graduate student in BCS, created WormWideWeb.org. The site, allowing researchers to easily browse and download C. elegans whole-brain datasets, will be useful to C. elegans neuroscientists and theoretical/computational neuroscientists. 
      Paul Berube, research scientist in the Department of Civil and Environmental Engineering, and Steven Biller, assistant professor of biological sciences at Wellesley College, won for “Unlocking Marine Microbiomes with Open Data.” Open data of genomes and metagenomes for marine ecosystems, with a focus on cyanobacteria, leverage the power of contemporaneous data from GEOTRACES and other long-standing ocean time-series programs to provide underlying information to answer questions about marine ecosystem function. 
      Jack Cavanagh, Sarah Kopper, and Diana Horvath of the Abdul Latif Jameel Poverty Action Lab (J-PAL) were recognized for J-PAL’s Data Publication Infrastructure, which includes a trusted repository of open-access datasets, a dedicated team of data curators, and coding tools and training materials to help other teams publish data in an efficient and ethical manner. 
      Jerome Patrick Cruz, graduate student in the Department of Political Science, won for OpenAudit, leveraging advances in natural language processing and machine learning to make data in public audit reports more usable for academics and policy researchers, as well as governance practitioners, watchdogs, and reformers. This work was done in collaboration with colleagues at Ateneo de Manila University in the Philippines. 
      Undergraduate student Daniel Kurlander created a tool for planetary scientists to rapidly access and filter images of the comet 67P/Churyumov-Gerasimenko. The web-based tool enables searches by location and other properties, does not require a time-intensive download of a massive dataset, allows analysis of the data independent of the speed of one’s computer, and does not require installation of a complex set of programs. 
      Halie Olson, postdoc in BCS, was recognized for sharing data from a functional magnetic resonance imaging (fMRI) study on language processing. The study used video clips from “Sesame Street” in which researchers manipulated the comprehensibility of the speech stream, allowing them to isolate a “language response” in the brain.
      Thomas González Roberts, graduate student in the Department of Aeronautics and Astronautics, won for the International Telecommunication Union Compliance Assessment Monitor. This tool combats the heritage of secrecy in outer space operations by creating human- and machine-readable datasets that succinctly describe the international agreements that govern satellite operations. 
      Melissa Kline Struhl, research scientist in BCS, was recognized for Children Helping Science, a free, open-source platform for remote studies with babies and children that makes it possible for researchers at more than 100 institutions to conduct reproducible studies. 
      JS Tan, graduate student in the Department of Urban Studies and Planning, developed the Collective Action in Tech Archive in collaboration with Nataliya Nedzhvetskaya of the University of California at Berkeley. It is an open database of all publicly recorded collective actions taken by workers in the global tech industry. 
      A complete list of winning projects and honorable mentions, including links to the research data, is available on the MIT Libraries website. 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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      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

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      A faster way to teach a robot

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      Imagine purchasing a robot to perform household tasks. This robot was built and trained in a factory on a certain set of tasks and has never seen the items in your home. When you ask it to pick up a mug from your kitchen table, it might not recognize your mug (perhaps because this mug is painted with an unusual image, say, of MIT’s mascot, Tim the Beaver). So, the robot fails.

      “Right now, the way we train these robots, when they fail, we don’t really know why. So you would just throw up your hands and say, ‘OK, I guess we have to start over.’ A critical component that is missing from this system is enabling the robot to demonstrate why it is failing so the user can give it feedback,” says Andi Peng, an electrical engineering and computer science (EECS) graduate student at MIT.

      Peng and her collaborators at MIT, New York University, and the University of California at Berkeley created a framework that enables humans to quickly teach a robot what they want it to do, with a minimal amount of effort.

      When a robot fails, the system uses an algorithm to generate counterfactual explanations that describe what needed to change for the robot to succeed. For instance, maybe the robot would have been able to pick up the mug if the mug were a certain color. It shows these counterfactuals to the human and asks for feedback on why the robot failed. Then the system utilizes this feedback and the counterfactual explanations to generate new data it uses to fine-tune the robot.

      Fine-tuning involves tweaking a machine-learning model that has already been trained to perform one task, so it can perform a second, similar task.

      The researchers tested this technique in simulations and found that it could teach a robot more efficiently than other methods. The robots trained with this framework performed better, while the training process consumed less of a human’s time.

      This framework could help robots learn faster in new environments without requiring a user to have technical knowledge. In the long run, this could be a step toward enabling general-purpose robots to efficiently perform daily tasks for the elderly or individuals with disabilities in a variety of settings.

      Peng, the lead author, is joined by co-authors Aviv Netanyahu, an EECS graduate student; Mark Ho, an assistant professor at the Stevens Institute of Technology; Tianmin Shu, an MIT postdoc; Andreea Bobu, a graduate student at UC Berkeley; and senior authors Julie Shah, an MIT professor of aeronautics and astronautics and the director of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), and Pulkit Agrawal, a professor in CSAIL. The research will be presented at the International Conference on Machine Learning.

      On-the-job training

      Robots often fail due to distribution shift — the robot is presented with objects and spaces it did not see during training, and it doesn’t understand what to do in this new environment.

      One way to retrain a robot for a specific task is imitation learning. The user could demonstrate the correct task to teach the robot what to do. If a user tries to teach a robot to pick up a mug, but demonstrates with a white mug, the robot could learn that all mugs are white. It may then fail to pick up a red, blue, or “Tim-the-Beaver-brown” mug.

      Training a robot to recognize that a mug is a mug, regardless of its color, could take thousands of demonstrations.

      “I don’t want to have to demonstrate with 30,000 mugs. I want to demonstrate with just one mug. But then I need to teach the robot so it recognizes that it can pick up a mug of any color,” Peng says.

      To accomplish this, the researchers’ system determines what specific object the user cares about (a mug) and what elements aren’t important for the task (perhaps the color of the mug doesn’t matter). It uses this information to generate new, synthetic data by changing these “unimportant” visual concepts. This process is known as data augmentation.

      The framework has three steps. First, it shows the task that caused the robot to fail. Then it collects a demonstration from the user of the desired actions and generates counterfactuals by searching over all features in the space that show what needed to change for the robot to succeed.

      The system shows these counterfactuals to the user and asks for feedback to determine which visual concepts do not impact the desired action. Then it uses this human feedback to generate many new augmented demonstrations.

      In this way, the user could demonstrate picking up one mug, but the system would produce demonstrations showing the desired action with thousands of different mugs by altering the color. It uses these data to fine-tune the robot.

      Creating counterfactual explanations and soliciting feedback from the user are critical for the technique to succeed, Peng says.

      From human reasoning to robot reasoning

      Because their work seeks to put the human in the training loop, the researchers tested their technique with human users. They first conducted a study in which they asked people if counterfactual explanations helped them identify elements that could be changed without affecting the task.

      “It was so clear right off the bat. Humans are so good at this type of counterfactual reasoning. And this counterfactual step is what allows human reasoning to be translated into robot reasoning in a way that makes sense,” she says.

      Then they applied their framework to three simulations where robots were tasked with: navigating to a goal object, picking up a key and unlocking a door, and picking up a desired object then placing it on a tabletop. In each instance, their method enabled the robot to learn faster than with other techniques, while requiring fewer demonstrations from users.

      Moving forward, the researchers hope to test this framework on real robots. They also want to focus on reducing the time it takes the system to create new data using generative machine-learning models.

      “We want robots to do what humans do, and we want them to do it in a semantically meaningful way. Humans tend to operate in this abstract space, where they don’t think about every single property in an image. At the end of the day, this is really about enabling a robot to learn a good, human-like representation at an abstract level,” Peng says.

      This research is supported, in part, by a National Science Foundation Graduate Research Fellowship, Open Philanthropy, an Apple AI/ML Fellowship, Hyundai Motor Corporation, the MIT-IBM Watson AI Lab, and the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions. More

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      3 Questions: Honing robot perception and mapping

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      Walking to a friend’s house or browsing the aisles of a grocery store might feel like simple tasks, but they in fact require sophisticated capabilities. That’s because humans are able to effortlessly understand their surroundings and detect complex information about patterns, objects, and their own location in the environment.

      What if robots could perceive their environment in a similar way? That question is on the minds of MIT Laboratory for Information and Decision Systems (LIDS) researchers Luca Carlone and Jonathan How. In 2020, a team led by Carlone released the first iteration of Kimera, an open-source library that enables a single robot to construct a three-dimensional map of its environment in real time, while labeling different objects in view. Last year, Carlone’s and How’s research groups (SPARK Lab and Aerospace Controls Lab) introduced Kimera-Multi, an updated system in which multiple robots communicate among themselves in order to create a unified map. A 2022 paper associated with the project recently received this year’s IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award, given to the best paper published in the journal in 2022.

      Carlone, who is the Leonardo Career Development Associate Professor of Aeronautics and Astronautics, and How, the Richard Cockburn Maclaurin Professor in Aeronautics and Astronautics, spoke to LIDS about Kimera-Multi and the future of how robots might perceive and interact with their environment.

      Q: Currently your labs are focused on increasing the number of robots that can work together in order to generate 3D maps of the environment. What are some potential advantages to scaling this system?

      How: The key benefit hinges on consistency, in the sense that a robot can create an independent map, and that map is self-consistent but not globally consistent. We’re aiming for the team to have a consistent map of the world; that’s the key difference in trying to form a consensus between robots as opposed to mapping independently.

      Carlone: In many scenarios it’s also good to have a bit of redundancy. For example, if we deploy a single robot in a search-and-rescue mission, and something happens to that robot, it would fail to find the survivors. If multiple robots are doing the exploring, there’s a much better chance of success. Scaling up the team of robots also means that any given task may be completed in a shorter amount of time.

      Q: What are some of the lessons you’ve learned from recent experiments, and challenges you’ve had to overcome while designing these systems?

      Carlone: Recently we did a big mapping experiment on the MIT campus, in which eight robots traversed up to 8 kilometers in total. The robots have no prior knowledge of the campus, and no GPS. Their main tasks are to estimate their own trajectory and build a map around it. You want the robots to understand the environment as humans do; humans not only understand the shape of obstacles, to get around them without hitting them, but also understand that an object is a chair, a desk, and so on. There’s the semantics part.

      The interesting thing is that when the robots meet each other, they exchange information to improve their map of the environment. For instance, if robots connect, they can leverage information to correct their own trajectory. The challenge is that if you want to reach a consensus between robots, you don’t have the bandwidth to exchange too much data. One of the key contributions of our 2022 paper is to deploy a distributed protocol, in which robots exchange limited information but can still agree on how the map looks. They don’t send camera images back and forth but only exchange specific 3D coordinates and clues extracted from the sensor data. As they continue to exchange such data, they can form a consensus.

      Right now we are building color-coded 3D meshes or maps, in which the color contains some semantic information, like “green” corresponds to grass, and “magenta” to a building. But as humans, we have a much more sophisticated understanding of reality, and we have a lot of prior knowledge about relationships between objects. For instance, if I was looking for a bed, I would go to the bedroom instead of exploring the entire house. If you start to understand the complex relationships between things, you can be much smarter about what the robot can do in the environment. We’re trying to move from capturing just one layer of semantics, to a more hierarchical representation in which the robots understand rooms, buildings, and other concepts.

      Q: What kinds of applications might Kimera and similar technologies lead to in the future?

      How: Autonomous vehicle companies are doing a lot of mapping of the world and learning from the environments they’re in. The holy grail would be if these vehicles could communicate with each other and share information, then they could improve models and maps that much quicker. The current solutions out there are individualized. If a truck pulls up next to you, you can’t see in a certain direction. Could another vehicle provide a field of view that your vehicle otherwise doesn’t have? This is a futuristic idea because it requires vehicles to communicate in new ways, and there are privacy issues to overcome. But if we could resolve those issues, you could imagine a significantly improved safety situation, where you have access to data from multiple perspectives, not only your field of view.

      Carlone: These technologies will have a lot of applications. Earlier I mentioned search and rescue. Imagine that you want to explore a forest and look for survivors, or map buildings after an earthquake in a way that can help first responders access people who are trapped. Another setting where these technologies could be applied is in factories. Currently, robots that are deployed in factories are very rigid. They follow patterns on the floor, and are not really able to understand their surroundings. But if you’re thinking about much more flexible factories in the future, robots will have to cooperate with humans and exist in a much less structured environment. More