More stories

  • in

    Inclusive research for social change

    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

  • in

    “MIT can give you ‘superpowers’”

    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

  • in

    Automated system teaches users when to collaborate with an AI assistant

    Artificial intelligence models that pick out patterns in images can often do so better than human eyes — but not always. If a radiologist is using an AI model to help her determine whether a patient’s X-rays show signs of pneumonia, when should she trust the model’s advice and when should she ignore it?

    A customized onboarding process could help this radiologist answer that question, according to researchers at MIT and the MIT-IBM Watson AI Lab. They designed a system that teaches a user when to collaborate with an AI assistant.

    In this case, the training method might find situations where the radiologist trusts the model’s advice — except she shouldn’t because the model is wrong. The system automatically learns rules for how she should collaborate with the AI, and describes them with natural language.

    During onboarding, the radiologist practices collaborating with the AI using training exercises based on these rules, receiving feedback about her performance and the AI’s performance.

    The researchers found that this onboarding procedure led to about a 5 percent improvement in accuracy when humans and AI collaborated on an image prediction task. Their results also show that just telling the user when to trust the AI, without training, led to worse performance.

    Importantly, the researchers’ system is fully automated, so it learns to create the onboarding process based on data from the human and AI performing a specific task. It can also adapt to different tasks, so it can be scaled up and used in many situations where humans and AI models work together, such as in social media content moderation, writing, and programming.

    “So often, people are given these AI tools to use without any training to help them figure out when it is going to be helpful. That’s not what we do with nearly every other tool that people use — there is almost always some kind of tutorial that comes with it. But for AI, this seems to be missing. We are trying to tackle this problem from a methodological and behavioral perspective,” says Hussein Mozannar, a graduate student in the Social and Engineering Systems doctoral program within the Institute for Data, Systems, and Society (IDSS) and lead author of a paper about this training process.

    The researchers envision that such onboarding will be a crucial part of training for medical professionals.

    “One could imagine, for example, that doctors making treatment decisions with the help of AI will first have to do training similar to what we propose. We may need to rethink everything from continuing medical education to the way clinical trials are designed,” says senior author David Sontag, a professor of EECS, a member of the MIT-IBM Watson AI Lab and the MIT Jameel Clinic, and the leader of the Clinical Machine Learning Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

    Mozannar, who is also a researcher with the Clinical Machine Learning Group, is joined on the paper by Jimin J. Lee, an undergraduate in electrical engineering and computer science; Dennis Wei, a senior research scientist at IBM Research; and Prasanna Sattigeri and Subhro Das, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented at the Conference on Neural Information Processing Systems.

    Training that evolves

    Existing onboarding methods for human-AI collaboration are often composed of training materials produced by human experts for specific use cases, making them difficult to scale up. Some related techniques rely on explanations, where the AI tells the user its confidence in each decision, but research has shown that explanations are rarely helpful, Mozannar says.

    “The AI model’s capabilities are constantly evolving, so the use cases where the human could potentially benefit from it are growing over time. At the same time, the user’s perception of the model continues changing. So, we need a training procedure that also evolves over time,” he adds.

    To accomplish this, their onboarding method is automatically learned from data. It is built from a dataset that contains many instances of a task, such as detecting the presence of a traffic light from a blurry image.

    The system’s first step is to collect data on the human and AI performing this task. In this case, the human would try to predict, with the help of AI, whether blurry images contain traffic lights.

    The system embeds these data points onto a latent space, which is a representation of data in which similar data points are closer together. It uses an algorithm to discover regions of this space where the human collaborates incorrectly with the AI. These regions capture instances where the human trusted the AI’s prediction but the prediction was wrong, and vice versa.

    Perhaps the human mistakenly trusts the AI when images show a highway at night.

    After discovering the regions, a second algorithm utilizes a large language model to describe each region as a rule, using natural language. The algorithm iteratively fine-tunes that rule by finding contrasting examples. It might describe this region as “ignore AI when it is a highway during the night.”

    These rules are used to build training exercises. The onboarding system shows an example to the human, in this case a blurry highway scene at night, as well as the AI’s prediction, and asks the user if the image shows traffic lights. The user can answer yes, no, or use the AI’s prediction.

    If the human is wrong, they are shown the correct answer and performance statistics for the human and AI on these instances of the task. The system does this for each region, and at the end of the training process, repeats the exercises the human got wrong.

    “After that, the human has learned something about these regions that we hope they will take away in the future to make more accurate predictions,” Mozannar says.

    Onboarding boosts accuracy

    The researchers tested this system with users on two tasks — detecting traffic lights in blurry images and answering multiple choice questions from many domains (such as biology, philosophy, computer science, etc.).

    They first showed users a card with information about the AI model, how it was trained, and a breakdown of its performance on broad categories. Users were split into five groups: Some were only shown the card, some went through the researchers’ onboarding procedure, some went through a baseline onboarding procedure, some went through the researchers’ onboarding procedure and were given recommendations of when they should or should not trust the AI, and others were only given the recommendations.

    Only the researchers’ onboarding procedure without recommendations improved users’ accuracy significantly, boosting their performance on the traffic light prediction task by about 5 percent without slowing them down. However, onboarding was not as effective for the question-answering task. The researchers believe this is because the AI model, ChatGPT, provided explanations with each answer that convey whether it should be trusted.

    But providing recommendations without onboarding had the opposite effect — users not only performed worse, they took more time to make predictions.

    “When you only give someone recommendations, it seems like they get confused and don’t know what to do. It derails their process. People also don’t like being told what to do, so that is a factor as well,” Mozannar says.

    Providing recommendations alone could harm the user if those recommendations are wrong, he adds. With onboarding, on the other hand, the biggest limitation is the amount of available data. If there aren’t enough data, the onboarding stage won’t be as effective, he says.

    In the future, he and his collaborators want to conduct larger studies to evaluate the short- and long-term effects of onboarding. They also want to leverage unlabeled data for the onboarding process, and find methods to effectively reduce the number of regions without omitting important examples.

    “People are adopting AI systems willy-nilly, and indeed AI offers great potential, but these AI agents still sometimes makes mistakes. Thus, it’s crucial for AI developers to devise methods that help humans know when it’s safe to rely on the AI’s suggestions,” says Dan Weld, professor emeritus at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, who was not involved with this research. “Mozannar et al. have created an innovative method for identifying situations where the AI is trustworthy, and (importantly) to describe them to people in a way that leads to better human-AI team interactions.”

    This work is funded, in part, by the MIT-IBM Watson AI Lab. More

  • in

    AI accelerates problem-solving in complex scenarios

    While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

    This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

    The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

    Researchers from MIT and ETH Zurich used machine learning to speed things up.

    They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

    Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

    This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

    This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

    “Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

    Wu wrote the paper with co-lead authors Siriu Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

    Tough to solve

    MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

    “These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

    An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

    A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

    Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

    Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

    “Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

    Shrinking the solution space

    She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

    Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

    This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

    The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

    This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

    In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

    This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee. More

  • in

    Rewarding excellence in open data

    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

  • in

    Forging climate connections across the Institute

    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

  • in

    Learning how to learn

    Suppose you need to be on today’s only ferry to Martha’s Vineyard, which leaves at 2 p.m. It takes about 30 minutes (on average) to drive from where you are to the terminal. What time should you leave?

    This is one of many common real-life examples used by Richard “Dick” Larson, a post-tenure professor in the MIT Institute for Data, Systems, and Society (IDSS), to explore exemplary problem-solving in his new book “Model Thinking for Everyday Life: How to Make Smarter Decisions.”

    Larson’s book synthesizes a lifelong career as an MIT professor and researcher, highlighting crucial skills underpinning all empirical, rational, and critical thinking. “Critical thinkers are energetic detectives … always seeking the facts,” he says. “Additional facts may surface that can result in modified conclusions … A critical thinker is aware of the pitfalls of human intuition.”

    For Larson, “model” thinking means not only thinking aided by conceptual and/or mathematical models, but a broader mode of critical thought that is informed by STEM concepts and worthy of emulation.

    In the ferry example, a key concept at play is uncertainty. Accounting for uncertainty is a core challenge faced by systems engineers, operations researchers, and modelers of complex networks — all hats Larson has worn in over half a century at MIT. 

    Uncertainty complicates all prediction and decision-making, and while statistics offers tactics for managing uncertainty, “Model Thinking” is not a math textbook. There are equations for the math-curious, but it doesn’t take a degree from MIT to understand that

    an average of 30 minutes would cover a range of times, some shorter, some longer;
    outliers can exist in the data, like the time construction traffic added an additional 30 minutes
    “about 30 minutes” is a prediction based on past experience, not current information (road closures, accidents, etc.); and
    the consequence for missing the ferry is not a delay of hours, but a full day — which might completely disrupt the trip or its purpose.
    And so, without doing much explicit math, you calculate variables, weigh the likelihood of different outcomes against the consequences of failure, and choose a departure time. Larson’s conclusion is one championed by dads everywhere: Leave on the earlier side, just in case. 

    “The world’s most important, invisible profession”

    Throughout Larson’s career at MIT, he has focused on the science of solving problems and making better decisions. “Faced with a new problem, people often lack the ability to frame and formulate it using basic principles,” argues Larson. “Our emphasis is on problem framing and formulation, with mathematics and physics playing supporting roles.”

    This is operations research, which Larson calls “the world’s most important invisible profession.” Formalized as a field during World War II, operations researchers use data and models to try to derive the “physics” of complex systems. The goal is typically optimizing things like scheduling, routing, simulation, prediction, planning, logistics, and queueing, for which Larson is especially well-known. A frequent media expert on the subject, he earned the moniker “Dr. Q” — and his research has led to new approaches for easing congestion in urban traffic, fast-food lines, and banks.

    Larson’s experience with complex systems provides a wealth of examples to draw on, but he is keen to demonstrate that his purview includes everyday decisions, and that “Model Thinking” is a book for everyone. 

    “Everybody uses models, whether they realize it or not,” he says. “If you have a bunch of errands to do, and you try to plan out the order to do them so you don’t have to drive as much, that’s more or less the ‘traveling salesman’ problem, a classic from operations research. Or when someone is shopping for groceries and thinking about how much of each product they need — they’re basically using an inventory management model of their pantry.”

    Larson’s takeaway is that since we all use conceptual models for thinking, planning, and decision-making, then understanding how our minds use models, and learning to use them more intentionally, can lead to clearer thinking, better planning, and smarter decision-making — especially when they are grounded in principles drawn from math and physics.

    Passion for the process

    Teaching STEM principles has long been a mission of Larson’s, who co-founded MIT BLOSSOMS (Blended Learning Open Source Science or Math Studies) with his late wife, Mary Elizabeth Murray. BLOSSOMS provides free, interactive STEM lessons and videos for primary school students around the world. Some of the exercises in “Model Thinking” refer to these videos as well.

    “A child’s educational opportunities shouldn’t be limited by where they were born or the wealth of their parents,” says Larson of the enterprise. 

    It was also Murray who encouraged Larson to write “Model Thinking.” “She saw how excited I was about it,” he says. “I had the choice of writing a textbook on queuing, say, or something else. It didn’t excite me at all.”

    Larson’s passion is for the process, not the answer. Throughout the book, he marks off opportunities for active learning with an icon showing the two tools necessary to complete each task: a sharpened pencil and a blank sheet of paper. 

    “Many of us in the age of instant Google searches have lost the ability — or perhaps the patience — to undertake multistep problems,” he argues.

    Model thinkers, on the other hand, understand and remember solutions better for having thought through the steps, and can better apply what they’ve learned to future problems. Larson’s “homework” is to do critical thinking, not just read about it. By working through thought experiments and scenarios, readers can achieve a deeper understanding of concepts like selection bias, random incidence, and orders of magnitude, all of which can present counterintuitive examples to the uninitiated.

    For Larson, who jokes that he is “an evangelist for models,” there is no better way to learn than by doing — except perhaps to teach. “Teaching a difficult topic is our best way to learn it ourselves, is an unselfish act, and bonds the teacher and learner,” he writes.

    In his long career as an educator and education advocate, Larson says he has always remained a learner himself. His love for learning illuminates every page of “Model Thinking,” which he hopes will provide others with the enjoyment and satisfaction that comes from learning new things and solving complex problems.

    “You will learn how to learn,” Larson says. “And you will enjoy it!” More

  • in

    A more effective experimental design for engineering a cell into a new state

    A strategy for cellular reprogramming involves using targeted genetic interventions to engineer a cell into a new state. The technique holds great promise in immunotherapy, for instance, where researchers could reprogram a patient’s T-cells so they are more potent cancer killers. Someday, the approach could also help identify life-saving cancer treatments or regenerative therapies that repair disease-ravaged organs.

    But the human body has about 20,000 genes, and a genetic perturbation could be on a combination of genes or on any of the over 1,000 transcription factors that regulate the genes. Because the search space is vast and genetic experiments are costly, scientists often struggle to find the ideal perturbation for their particular application.   

    Researchers from MIT and Harvard University developed a new, computational approach that can efficiently identify optimal genetic perturbations based on a much smaller number of experiments than traditional methods.

    Their algorithmic technique leverages the cause-and-effect relationship between factors in a complex system, such as genome regulation, to prioritize the best intervention in each round of sequential experiments.

    The researchers conducted a rigorous theoretical analysis to determine that their technique did, indeed, identify optimal interventions. With that theoretical framework in place, they applied the algorithms to real biological data designed to mimic a cellular reprogramming experiment. Their algorithms were the most efficient and effective.

    “Too often, large-scale experiments are designed empirically. A careful causal framework for sequential experimentation may allow identifying optimal interventions with fewer trials, thereby reducing experimental costs,” says co-senior author Caroline Uhler, a professor in the Department of Electrical Engineering and Computer Science (EECS) who is also co-director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems and Society (IDSS).

    Joining Uhler on the paper, which appears today in Nature Machine Intelligence, are lead author Jiaqi Zhang, a graduate student and Eric and Wendy Schmidt Center Fellow; co-senior author Themistoklis P. Sapsis, professor of mechanical and ocean engineering at MIT and a member of IDSS; and others at Harvard and MIT.

    Active learning

    When scientists try to design an effective intervention for a complex system, like in cellular reprogramming, they often perform experiments sequentially. Such settings are ideally suited for the use of a machine-learning approach called active learning. Data samples are collected and used to learn a model of the system that incorporates the knowledge gathered so far. From this model, an acquisition function is designed — an equation that evaluates all potential interventions and picks the best one to test in the next trial.

    This process is repeated until an optimal intervention is identified (or resources to fund subsequent experiments run out).

    “While there are several generic acquisition functions to sequentially design experiments, these are not effective for problems of such complexity, leading to very slow convergence,” Sapsis explains.

    Acquisition functions typically consider correlation between factors, such as which genes are co-expressed. But focusing only on correlation ignores the regulatory relationships or causal structure of the system. For instance, a genetic intervention can only affect the expression of downstream genes, but a correlation-based approach would not be able to distinguish between genes that are upstream or downstream.

    “You can learn some of this causal knowledge from the data and use that to design an intervention more efficiently,” Zhang explains.

    The MIT and Harvard researchers leveraged this underlying causal structure for their technique. First, they carefully constructed an algorithm so it can only learn models of the system that account for causal relationships.

    Then the researchers designed the acquisition function so it automatically evaluates interventions using information on these causal relationships. They crafted this function so it prioritizes the most informative interventions, meaning those most likely to lead to the optimal intervention in subsequent experiments.

    “By considering causal models instead of correlation-based models, we can already rule out certain interventions. Then, whenever you get new data, you can learn a more accurate causal model and thereby further shrink the space of interventions,” Uhler explains.

    This smaller search space, coupled with the acquisition function’s special focus on the most informative interventions, is what makes their approach so efficient.

    The researchers further improved their acquisition function using a technique known as output weighting, inspired by the study of extreme events in complex systems. This method carefully emphasizes interventions that are likely to be closer to the optimal intervention.

    “Essentially, we view an optimal intervention as an ‘extreme event’ within the space of all possible, suboptimal interventions and use some of the ideas we have developed for these problems,” Sapsis says.    

    Enhanced efficiency

    They tested their algorithms using real biological data in a simulated cellular reprogramming experiment. For this test, they sought a genetic perturbation that would result in a desired shift in average gene expression. Their acquisition functions consistently identified better interventions than baseline methods through every step in the multi-stage experiment.

    “If you cut the experiment off at any stage, ours would still be more efficient than the baselines. This means you could run fewer experiments and get the same or better results,” Zhang says.

    The researchers are currently working with experimentalists to apply their technique toward cellular reprogramming in the lab.

    Their approach could also be applied to problems outside genomics, such as identifying optimal prices for consumer products or enabling optimal feedback control in fluid mechanics applications.

    In the future, they plan to enhance their technique for optimizations beyond those that seek to match a desired mean. In addition, their method assumes that scientists already understand the causal relationships in their system, but future work could explore how to use AI to learn that information, as well.

    This work was funded, in part, by the Office of Naval Research, the MIT-IBM Watson AI Lab, the MIT J-Clinic for Machine Learning and Health, the Eric and Wendy Schmidt Center at the Broad Institute, a Simons Investigator Award, the Air Force Office of Scientific Research, and a National Science Foundation Graduate Fellowship. More