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    Using generative AI to improve software testing

    Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.

    Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive.

    For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.

    The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders — Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki ’15, SM ’16 — believe the company’s success is due to SDV’s ability to revolutionize software testing.

    SDV goes viral

    In 2016, Veeramachaneni’s group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.

    Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.

    Veeramachaneni’s group came across the problem because it was working with companies that wanted to share their data for research.

    “MIT helps you see all these different use cases,” Patki explains. “You work with finance companies and health care companies, and all those projects are useful to formulate solutions across industries.”

    In 2020, the researchers founded DataCebo to build more SDV features for larger organizations. Since then, the use cases have been as impressive as they’ve been varied.

    With DataCebo’s new flight simulator, for instance, airlines can plan for rare weather events in a way that would be impossible using only historic data. In another application, SDV users synthesized medical records to predict health outcomes for patients with cystic fibrosis. A team from Norway recently used SDV to create synthetic student data to evaluate whether various admissions policies were meritocratic and free from bias.

    In 2021, the data science platform Kaggle hosted a competition for data scientists that used SDV to create synthetic data sets to avoid using proprietary data. Roughly 30,000 data scientists participated, building solutions and predicting outcomes based on the company’s realistic data.

    And as DataCebo has grown, it’s stayed true to its MIT roots: All of the company’s current employees are MIT alumni.

    Supercharging software testing

    Although their open-source tools are being used for a variety of use cases, the company is focused on growing its traction in software testing.

    “You need data to test these software applications,” Veeramachaneni says. “Traditionally, developers manually write scripts to create synthetic data. With generative models, created using SDV, you can learn from a sample of data collected and then sample a large volume of synthetic data (which has the same properties as real data), or create specific scenarios and edge cases, and use the data to test your application.”

    For example, if a bank wanted to test a program designed to reject transfers from accounts with no money in them, it would have to simulate many accounts simultaneously transacting. Doing that with data created manually would take a lot of time. With DataCebo’s generative models, customers can create any edge case they want to test.

    “It’s common for industries to have data that is sensitive in some capacity,” Patki says. “Often when you’re in a domain with sensitive data you’re dealing with regulations, and even if there aren’t legal regulations, it’s in companies’ best interest to be diligent about who gets access to what at which time. So, synthetic data is always better from a privacy perspective.”

    Scaling synthetic data

    Veeramachaneni believes DataCebo is advancing the field of what it calls synthetic enterprise data, or data generated from user behavior on large companies’ software applications.

    “Enterprise data of this kind is complex, and there is no universal availability of it, unlike language data,” Veeramachaneni says. “When folks use our publicly available software and report back if works on a certain pattern, we learn a lot of these unique patterns, and it allows us to improve our algorithms. From one perspective, we are building a corpus of these complex patterns, which for language and images is readily available. “

    DataCebo also recently released features to improve SDV’s usefulness, including tools to assess the “realism” of the generated data, called the SDMetrics library as well as a way to compare models’ performances called SDGym.

    “It’s about ensuring organizations trust this new data,” Veeramachaneni says. “[Our tools offer] programmable synthetic data, which means we allow enterprises to insert their specific insight and intuition to build more transparent models.”

    As companies in every industry rush to adopt AI and other data science tools, DataCebo is ultimately helping them do so in a way that is more transparent and responsible.

    “In the next few years, synthetic data from generative models will transform all data work,” Veeramachaneni says. “We believe 90 percent of enterprise operations can be done with synthetic data.” More

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    Dealing with the limitations of our noisy world

    Tamara Broderick first set foot on MIT’s campus when she was a high school student, as a participant in the inaugural Women’s Technology Program. The monthlong summer academic experience gives young women a hands-on introduction to engineering and computer science.

    What is the probability that she would return to MIT years later, this time as a faculty member?

    That’s a question Broderick could probably answer quantitatively using Bayesian inference, a statistical approach to probability that tries to quantify uncertainty by continuously updating one’s assumptions as new data are obtained.

    In her lab at MIT, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS) uses Bayesian inference to quantify uncertainty and measure the robustness of data analysis techniques.

    “I’ve always been really interested in understanding not just ‘What do we know from data analysis,’ but ‘How well do we know it?’” says Broderick, who is also a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society. “The reality is that we live in a noisy world, and we can’t always get exactly the data that we want. How do we learn from data but at the same time recognize that there are limitations and deal appropriately with them?”

    Broadly, her focus is on helping people understand the confines of the statistical tools available to them and, sometimes, working with them to craft better tools for a particular situation.

    For instance, her group recently collaborated with oceanographers to develop a machine-learning model that can make more accurate predictions about ocean currents. In another project, she and others worked with degenerative disease specialists on a tool that helps severely motor-impaired individuals utilize a computer’s graphical user interface by manipulating a single switch.

    A common thread woven through her work is an emphasis on collaboration.

    “Working in data analysis, you get to hang out in everybody’s backyard, so to speak. You really can’t get bored because you can always be learning about some other field and thinking about how we can apply machine learning there,” she says.

    Hanging out in many academic “backyards” is especially appealing to Broderick, who struggled even from a young age to narrow down her interests.

    A math mindset

    Growing up in a suburb of Cleveland, Ohio, Broderick had an interest in math for as long as she can remember. She recalls being fascinated by the idea of what would happen if you kept adding a number to itself, starting with 1+1=2 and then 2+2=4.

    “I was maybe 5 years old, so I didn’t know what ‘powers of two’ were or anything like that. I was just really into math,” she says.

    Her father recognized her interest in the subject and enrolled her in a Johns Hopkins program called the Center for Talented Youth, which gave Broderick the opportunity to take three-week summer classes on a range of subjects, from astronomy to number theory to computer science.

    Later, in high school, she conducted astrophysics research with a postdoc at Case Western University. In the summer of 2002, she spent four weeks at MIT as a member of the first class of the Women’s Technology Program.

    She especially enjoyed the freedom offered by the program, and its focus on using intuition and ingenuity to achieve high-level goals. For instance, the cohort was tasked with building a device with LEGOs that they could use to biopsy a grape suspended in Jell-O.

    The program showed her how much creativity is involved in engineering and computer science, and piqued her interest in pursuing an academic career.

    “But when I got into college at Princeton, I could not decide — math, physics, computer science — they all seemed super-cool. I wanted to do all of it,” she says.

    She settled on pursuing an undergraduate math degree but took all the physics and computer science courses she could cram into her schedule.

    Digging into data analysis

    After receiving a Marshall Scholarship, Broderick spent two years at Cambridge University in the United Kingdom, earning a master of advanced study in mathematics and a master of philosophy in physics.

    In the UK, she took a number of statistics and data analysis classes, including her first class on Bayesian data analysis in the field of machine learning.

    It was a transformative experience, she recalls.

    “During my time in the U.K., I realized that I really like solving real-world problems that matter to people, and Bayesian inference was being used in some of the most important problems out there,” she says.

    Back in the U.S., Broderick headed to the University of California at Berkeley, where she joined the lab of Professor Michael I. Jordan as a grad student. She earned a PhD in statistics with a focus on Bayesian data analysis. 

    She decided to pursue a career in academia and was drawn to MIT by the collaborative nature of the EECS department and by how passionate and friendly her would-be colleagues were.

    Her first impressions panned out, and Broderick says she has found a community at MIT that helps her be creative and explore hard, impactful problems with wide-ranging applications.

    “I’ve been lucky to work with a really amazing set of students and postdocs in my lab — brilliant and hard-working people whose hearts are in the right place,” she says.

    One of her team’s recent projects involves a collaboration with an economist who studies the use of microcredit, or the lending of small amounts of money at very low interest rates, in impoverished areas.

    The goal of microcredit programs is to raise people out of poverty. Economists run randomized control trials of villages in a region that receive or don’t receive microcredit. They want to generalize the study results, predicting the expected outcome if one applies microcredit to other villages outside of their study.

    But Broderick and her collaborators have found that results of some microcredit studies can be very brittle. Removing one or a few data points from the dataset can completely change the results. One issue is that researchers often use empirical averages, where a few very high or low data points can skew the results.

    Using machine learning, she and her collaborators developed a method that can determine how many data points must be dropped to change the substantive conclusion of the study. With their tool, a scientist can see how brittle the results are.

    “Sometimes dropping a very small fraction of data can change the major results of a data analysis, and then we might worry how far those conclusions generalize to new scenarios. Are there ways we can flag that for people? That is what we are getting at with this work,” she explains.

    At the same time, she is continuing to collaborate with researchers in a range of fields, such as genetics, to understand the pros and cons of different machine-learning techniques and other data analysis tools.

    Happy trails

    Exploration is what drives Broderick as a researcher, and it also fuels one of her passions outside the lab. She and her husband enjoy collecting patches they earn by hiking all the trails in a park or trail system.

    “I think my hobby really combines my interests of being outdoors and spreadsheets,” she says. “With these hiking patches, you have to explore everything and then you see areas you wouldn’t normally see. It is adventurous, in that way.”

    They’ve discovered some amazing hikes they would never have known about, but also embarked on more than a few “total disaster hikes,” she says. But each hike, whether a hidden gem or an overgrown mess, offers its own rewards.

    And just like in her research, curiosity, open-mindedness, and a passion for problem-solving have never led her astray. More

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    Generating opportunities with generative AI

    Talking with retail executives back in 2010, Rama Ramakrishnan came to two realizations. First, although retail systems that offered customers personalized recommendations were getting a great deal of attention, these systems often provided little payoff for retailers. Second, for many of the firms, most customers shopped only once or twice a year, so companies didn’t really know much about them.

    “But by being very diligent about noting down the interactions a customer has with a retailer or an e-commerce site, we can create a very nice and detailed composite picture of what that person does and what they care about,” says Ramakrishnan, professor of the practice at the MIT Sloan School of Management. “Once you have that, then you can apply proven algorithms from machine learning.”

    These realizations led Ramakrishnan to found CQuotient, a startup whose software has now become the foundation for Salesforce’s widely adopted AI e-commerce platform. “On Black Friday alone, CQuotient technology probably sees and interacts with over a billion shoppers on a single day,” he says.

    After a highly successful entrepreneurial career, in 2019 Ramakrishnan returned to MIT Sloan, where he had earned master’s and PhD degrees in operations research in the 1990s. He teaches students “not just how these amazing technologies work, but also how do you take these technologies and actually put them to use pragmatically in the real world,” he says.

    Additionally, Ramakrishnan enjoys participating in MIT executive education. “This is a great opportunity for me to convey the things that I have learned, but also as importantly, to learn what’s on the minds of these senior executives, and to guide them and nudge them in the right direction,” he says.

    For example, executives are understandably concerned about the need for massive amounts of data to train machine learning systems. He can now guide them to a wealth of models that are pre-trained for specific tasks. “The ability to use these pre-trained AI models, and very quickly adapt them to your particular business problem, is an incredible advance,” says Ramakrishnan.

    Rama Ramakrishnan – Utilizing AI in Real World Applications for Intelligent WorkVideo: MIT Industrial Liaison Program

    Understanding AI categories

    “AI is the quest to imbue computers with the ability to do cognitive tasks that typically only humans can do,” he says. Understanding the history of this complex, supercharged landscape aids in exploiting the technologies.

    The traditional approach to AI, which basically solved problems by applying if/then rules learned from humans, proved useful for relatively few tasks. “One reason is that we can do lots of things effortlessly, but if asked to explain how we do them, we can’t actually articulate how we do them,” Ramakrishnan comments. Also, those systems may be baffled by new situations that don’t match up to the rules enshrined in the software.

    Machine learning takes a dramatically different approach, with the software fundamentally learning by example. “You give it lots of examples of inputs and outputs, questions and answers, tasks and responses, and get the computer to automatically learn how to go from the input to the output,” he says. Credit scoring, loan decision-making, disease prediction, and demand forecasting are among the many tasks conquered by machine learning.

    But machine learning only worked well when the input data was structured, for instance in a spreadsheet. “If the input data was unstructured, such as images, video, audio, ECGs, or X-rays, it wasn’t very good at going from that to a predicted output,” Ramakrishnan says. That means humans had to manually structure the unstructured data to train the system.

    Around 2010 deep learning began to overcome that limitation, delivering the ability to directly work with unstructured input data, he says. Based on a longstanding AI strategy known as neural networks, deep learning became practical due to the global flood tide of data, the availability of extraordinarily powerful parallel processing hardware called graphics processing units (originally invented for video games) and advances in algorithms and math.

    Finally, within deep learning, the generative AI software packages appearing last year can create unstructured outputs, such as human-sounding text, images of dogs, and three-dimensional models. Large language models (LLMs) such as OpenAI’s ChatGPT go from text inputs to text outputs, while text-to-image models such as OpenAI’s DALL-E can churn out realistic-appearing images.

    Rama Ramakrishnan – Making Note of Little Data to Improve Customer ServiceVideo: MIT Industrial Liaison Program

    What generative AI can (and can’t) do

    Trained on the unimaginably vast text resources of the internet, a LLM’s “fundamental capability is to predict the next most likely, most plausible word,” Ramakrishnan says. “Then it attaches the word to the original sentence, predicts the next word again, and keeps on doing it.”

    “To the surprise of many, including a lot of researchers, an LLM can do some very complicated things,” he says. “It can compose beautifully coherent poetry, write Seinfeld episodes, and solve some kinds of reasoning problems. It’s really quite remarkable how next-word prediction can lead to these amazing capabilities.”

    “But you have to always keep in mind that what it is doing is not so much finding the correct answer to your question as finding a plausible answer your question,” Ramakrishnan emphasizes. Its content may be factually inaccurate, irrelevant, toxic, biased, or offensive.

    That puts the burden on users to make sure that the output is correct, relevant, and useful for the task at hand. “You have to make sure there is some way for you to check its output for errors and fix them before it goes out,” he says.

    Intense research is underway to find techniques to address these shortcomings, adds Ramakrishnan, who expects many innovative tools to do so.

    Finding the right corporate roles for LLMs

    Given the astonishing progress in LLMs, how should industry think about applying the software to tasks such as generating content?

    First, Ramakrishnan advises, consider costs: “Is it a much less expensive effort to have a draft that you correct, versus you creating the whole thing?” Second, if the LLM makes a mistake that slips by, and the mistaken content is released to the outside world, can you live with the consequences?

    “If you have an application which satisfies both considerations, then it’s good to do a pilot project to see whether these technologies can actually help you with that particular task,” says Ramakrishnan. He stresses the need to treat the pilot as an experiment rather than as a normal IT project.

    Right now, software development is the most mature corporate LLM application. “ChatGPT and other LLMs are text-in, text-out, and a software program is just text-out,” he says. “Programmers can go from English text-in to Python text-out, as well as you can go from English-to-English or English-to-German. There are lots of tools which help you write code using these technologies.”

    Of course, programmers must make sure the result does the job properly. Fortunately, software development already offers infrastructure for testing and verifying code. “This is a beautiful sweet spot,” he says, “where it’s much cheaper to have the technology write code for you, because you can very quickly check and verify it.”

    Another major LLM use is content generation, such as writing marketing copy or e-commerce product descriptions. “Again, it may be much cheaper to fix ChatGPT’s draft than for you to write the whole thing,” Ramakrishnan says. “However, companies must be very careful to make sure there is a human in the loop.”

    LLMs also are spreading quickly as in-house tools to search enterprise documents. Unlike conventional search algorithms, an LLM chatbot can offer a conversational search experience, because it remembers each question you ask. “But again, it will occasionally make things up,” he says. “In terms of chatbots for external customers, these are very early days, because of the risk of saying something wrong to the customer.”

    Overall, Ramakrishnan notes, we’re living in a remarkable time to grapple with AI’s rapidly evolving potentials and pitfalls. “I help companies figure out how to take these very transformative technologies and put them to work, to make products and services much more intelligent, employees much more productive, and processes much more efficient,” he says. More

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    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

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    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

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    3 Questions: A new PhD program from the Center for Computational Science and Engineering

    This fall, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, is introducing a new standalone PhD degree program that will enable students to pursue research in cross-cutting methodological aspects of computational science and engineering. The launch follows approval of the center’s degree program proposal at the May 2023 Institute faculty meeting.

    Doctoral-level graduate study in computational science and engineering (CSE) at MIT has, for the past decade, been offered through an interdisciplinary program in which CSE students are admitted to one of eight participating academic departments in the School of Engineering or School of Science. While this model adds a strong disciplinary component to students’ education, the rapid growth of the CSE field and the establishment of the MIT Schwarzman College of Computing have prompted an exciting expansion of MIT’s graduate-level offerings in computation.

    The new degree, offered by the college, will run alongside MIT’s existing interdisciplinary offerings in CSE, complementing these doctoral training programs and preparing students to contribute to the leading edge of the field. Here, CCSE co-directors Youssef Marzouk and Nicolas Hadjiconstantinou discuss the standalone program and how they expect it to elevate the visibility and impact of CSE research and education at MIT.

    Q: What is computational science and engineering?

    Marzouk: Computational science and engineering focuses on the development and analysis of state-of-the-art methods for computation and their innovative application to problems of science and engineering interest. It has intellectual foundations in applied mathematics, statistics, and computer science, and touches the full range of science and engineering disciplines. Yet, it synthesizes these foundations into a discipline of its own — one that links the digital and physical worlds. It’s an exciting and evolving multidisciplinary field.

    Hadjiconstantinou: Examples of CSE research happening at MIT include modeling and simulation techniques, the underlying computational mathematics, and data-driven modeling of physical systems. Computational statistics and scientific machine learning have become prominent threads within CSE, joining high-performance computing, mathematically-oriented programming languages, and their broader links to algorithms and software. Application domains include energy, environment and climate, materials, health, transportation, autonomy, and aerospace, among others. Some of our researchers focus on general and widely applicable methodology, while others choose to focus on methods and algorithms motivated by a specific domain of application.

    Q: What was the motivation behind creating a standalone PhD program?

    Marzouk: The new degree focuses on a particular class of students whose background and interests are primarily in CSE methodology, in a manner that cuts across the disciplinary research structure represented by our current “with-departments” degree program. There is a strong research demand for such methodologically-focused students among CCSE faculty and MIT faculty in general. Our objective is to create a targeted, coherent degree program in this field that, alongside our other thriving CSE offerings, will create the leading environment for top CSE students worldwide.

    Hadjiconstantinou: One of CCSE’s most important functions is to recruit exceptional students who are trained in and want to work in computational science and engineering. Experience with our CSE master’s program suggests that students with a strong background and interests in the discipline prefer to apply to a pure CSE program for their graduate studies. The standalone degree aims to bring these students to MIT and make them available to faculty across the Institute.

    Q: How will this impact computing education and research at MIT? 

    Hadjiconstantinou: We believe that offering a standalone PhD program in CSE alongside the existing “with-departments” programs will significantly strengthen MIT’s graduate programs in computing. In particular, it will strengthen the methodological core of CSE research and education at MIT, while continuing to support the disciplinary-flavored CSE work taking place in our participating departments, which include Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Materials Science and Engineering; Mechanical Engineering; Nuclear Science and Engineering; Earth, Atmospheric and Planetary Sciences; and Mathematics. Together, these programs will create a stronger CSE student cohort and facilitate deeper exchanges between the college and other units at MIT.

    Marzouk: In a broader sense, the new program is designed to help realize one of the key opportunities presented by the college, which is to create a richer variety of graduate degrees in computation and to involve as many faculty and units in these educational endeavors as possible. The standalone CSE PhD will join other distinguished doctoral programs of the college — such as the Department of Electrical Engineering and Computer Science PhD; the Operations Research Center PhD; and the Interdisciplinary Doctoral Program in Statistics and the Social and Engineering Systems PhD within the Institute for Data, Systems, and Society — and grow in a way that is informed by them. The confluence of these academic programs, and natural synergies among them, will make MIT quite unique. More

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    Advancing social studies at MIT Sloan

    Around 2010, Facebook was a relatively small company with about 2,000 employees. So, when a PhD student named Dean Eckles showed up to serve an intership at the firm, he landed in a position with some real duties.

    Eckles essentially became the primary data scientist for the product manager who was overseeing the platform’s news feeds. That manager would pepper Eckles with questions. How exactly do people influence each other online? If Facebook tweaked its content-ranking algorithms, what would happen? What occurs when you show people more photos?

    As a doctoral candidate already studying social influence, Eckles was well-equipped to think about such questions, and being at Facebook gave him a lot of data to study them. 

    “If you show people more photos, they post more photos themselves,” Eckles says. “In turn, that affects the experience of all their friends. Plus they’re getting more likes and more comments. It affects everybody’s experience. But can you account for all of these compounding effects across the network?”

    Eckles, now an associate professor in the MIT Sloan School of Management and an affiliate faculty member of the Institute for Data, Systems, and Society, has made a career out of thinking carefully about that last question. Studying social networks allows Eckles to tackle significant questions involving, for example, the economic and political effects of social networks, the spread of misinformation, vaccine uptake during the Covid-19 crisis, and other aspects of the formation and shape of social networks. For instance, one study he co-authored this summer shows that people who either move between U.S. states, change high schools, or attend college out of state, wind up with more robust social networks, which are strongly associated with greater economic success.

    Eckles maintains another research channel focused on what scholars call “causal inference,” the methods and techniques that allow researchers to identify cause-and-effect connections in the world.

    “Learning about cause-and-effect relationships is core to so much science,” Eckles says. “In behavioral, social, economic, or biomedical science, it’s going to be hard. When you start thinking about humans, causality gets difficult. People do things strategically, and they’re electing into situations based on their own goals, so that complicates a lot of cause-and-effect relationships.”

    Eckles has now published dozens of papers in each of his different areas of work; for his research and teaching, Eckles received tenure from MIT last year.

    Five degrees and a job

    Eckles grew up in California, mostly near the Lake Tahoe area. He attended Stanford University as an undergraduate, arriving on campus in fall 2002 — and didn’t really leave for about a decade. Eckles has five degrees from Stanford. As an undergrad, he received a BA in philosophy and a BS in symbolic systems, an interdisciplinary major combining computer science, philosophy, psychology, and more. Eckles was set to attend Oxford University for graduate work in philosophy but changed his mind and stayed at Stanford for an MS in symbolic systems too. 

    “[Oxford] might have been a great experience, but I decided to focus more on the tech side of things,” he says.

    After receiving his first master’s degree, Eckles did take a year off from school and worked for Nokia, although the firm’s offices were adjacent to the Stanford campus and Eckles would sometimes stop and talk to faculty during the workday. Soon he was enrolled at Stanford again, this time earning his PhD in communication, in 2012, while receiving an MA in statistics the year before. His doctoral dissertation wound up being about peer influence in networks. PhD in hand, Eckles promptly headed back to Facebook, this time for three years as a full-time researcher.

     “They were really supportive of the work I was doing,” Eckles says.

    Still, Eckles remained interested in moving into academia, and joined the MIT faculty in 2017 with a position in MIT Sloan’s Marketing Group. The group consists of a set of scholars with far-ranging interests, from cognitive science to advertising to social network dynamics.

    “Our group reflects something deeper about the Sloan school and about MIT as well, an openness to doing things differently and not having to fit into narrowly defined tracks,” Eckles says.

    For that matter, MIT has many faculty in different domains who work on causal inference, and whose work Eckles quickly cites — including economists Victor Chernozhukov and Alberto Abadie, and Joshua Angrist, whose book “Mostly Harmless Econometrics” Eckles name-checks as an influence.

    “I’ve been fortunate in my career that causal inference turned out to be a hot area,” Eckles says. “But I think it’s hot for good reasons. People started to realize that, yes, causal inference is really important. There are economists, computer scientists, statisticians, and epidemiologists who are going to the same conferences and citing each other’s papers. There’s a lot happening.”

    How do networks form?

    These days, Eckles is interested in expanding the questions he works on. In the past, he has often studied existing social networks and looked at their effects. For instance: One study Eckles co-authored, examining the 2012 U.S. elections, found that get-out-the-vote messages work very well, especially when relayed via friends.

    That kind of study takes the existence of the network as a given, though. Another kind of research question is, as Eckles puts it, “How do social networks form and evolve? And what are the consequences of these network structures?” His recent study about social networks expanding as people move around and change schools is one example of research that digs into the core life experiences underlying social networks.

    “I’m excited about doing more on how these networks arise and what factors, including everything from personality to public transit, affect their formation,” Eckles says.

    Understanding more about how social networks form gets at key questions about social life and civic structure. Suppose research shows how some people develop and maintain beneficial connections in life; it’s possible that those insights could be applied to programs helping people in more disadvantaged situations realize some of the same opportunities.

    “We want to act on things,” Eckles says. “Sometimes people say, ‘We care about prediction.’ I would say, ‘We care about prediction under intervention.’ We want to predict what’s going to happen if we try different things.”

    Ultimately, Eckles reflects, “Trying to reason about the origins and maintenance of social networks, and the effects of networks, is interesting substantively and methodologically. Networks are super-high-dimensional objects, even just a single person’s network and all its connections. You have to summarize it, so for instance we talk about weak ties or strong ties, but do we have the correct description? There are fascinating questions that require development, and I’m eager to keep working on them.”   More

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    Supporting sustainability, digital health, and the future of work

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

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

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

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

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

    Accelerating the journey to net zero with industrial clusters

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

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

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

    Eliminating childhood obesity

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

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

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

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

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

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

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