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      The science of strength: How data analytics is transforming college basketball

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      In the 1990s, if you suggested that the corner three-pointer was the best shot in basketball, you might have been laughed out of the gym.

      The game was still dominated largely by a fleet of seven-foot centers, most of whom couldn’t shoot from more than a few feet out from the basket. Even the game’s best player, Michael Jordan, was a mid-range specialist who averaged under two three-point attempts per game for his career.

      Fast forward to today, and the best players average around a dozen long-ball attempts per game — typically favoring shots from the corner.

      What’s changed? Analytics.

      “When I first started in the profession, 10 to 12 years ago, data analytics was almost nonexistent in training rooms,” says Adam Petway, the director of strength and conditioning for men’s basketball at the University of Louisville. “Today, we have force platform technology, we have velocity-based training, we have GPS tracking during games and in training, all to get a more objective analysis to help our athletes. So it’s grown exponentially.”

      Petway, who previously worked on the coaching staffs of the NBA’s Philadelphia 76ers and Washington Wizards, holds a bachelor’s degree in sports science, an MBA with an emphasis in sport management, and a doctorate in sports science. Recently, he extended his education through MIT Professional Education’s Applied Data Science Program (ADSP).

      “The impetus behind enrolling in ADSP was primarily a curiosity to learn and a desire to get better,” Petway says. “In my time in pro and college sports, we’ve had whole departments dedicated to data science, so I know it’s a skill set I’ll need in the future.”

      Applying new skills

      Petway took classes in a live online format. Although he was the only strength and conditioning coach in his cohort — learning alongside lawyers, professors, and business executives — he says that the focus on data gave all of his classmates a common language of sorts.

      “In many people’s minds, the worlds of data science and NCAA strength and conditioning training may not cross. We are finding that there are many other professional and industry sectors that can benefit from data science and analytics, which explains why we are seeing an ever-growing range of professionals from around the globe enroll in our Applied Data Science Program,” says Bhaskar Pant, executive director of MIT Professional Education. “It’s exciting to hear how change-makers like Adam are using the knowledge they gained from the program to tackle their most pressing challenges using data science tools.”

      “Having access to such high-level practitioners within data science was something that I found very, very helpful,” Petway says. “The chance to interact with my classmates, and the chance to interact in small groups with the professionals and the professors, was unbelievable. When you’re writing code in Python you might mess up a semicolon and a comma, and get 200 characters into the code and realize that it’s not going to work. So the ability to stop and ask questions, and really get into the material with a cohort of peers from different industries, that was really helpful.”

      Petway points to his newfound abilities to code in Python, and to run data through artificial intelligence programs that utilize unsupervised learning techniques, as major takeaways from his experience. Sports teams produce a wealth of data, he notes, but coaches need to be able to process that information in ways that lead to actionable insights.

      “Now I’m able to create decision trees, do visualization with data, and run a principal component analysis,” Petway says. “So instead of relying on third-party companies to come in and tell me what to do, I can take all of that data and disseminate the results myself, which not only saves me time, but it saves a lot of money.”

      In addition to giving him new capabilities in his coaching role, the skills were crucial to the research for a paper that Petway and a team of several other authors published in the International Journal of Strength and Conditioning this year. “The data came from my PhD program around five years ago,” Petway notes. “I had the data already, but I couldn’t properly visualize it and analyze it until I took the MIT Professional Education course.”

      “MIT’s motto is ‘mens et manus’ (‘mind and hand’), which translates to experience-based learning. As such, there was great thought put into how the Applied Data Science Program is structured. The expectation is that every participant not only gains foundational skills, but also learns how to apply that knowledge in real-world scenarios. We are thrilled to see learning from our course applied to top-level college basketball,” says Munther Dahleh, director of the Institute for Data, Systems, and Society, the William A. Coolidge Professor of Electrical Engineering and Computer Science at MIT, and one of the instructors of ADSP.

      Data’s growing role in sports

      Analytics are pushing the field of strength and conditioning far beyond the days when trainers would simply tell players to do a certain number of reps in the weight room, Petway says. Wearable devices help to track how much ground athletes cover during practice, as well as their average speed. Data from a force platform helps Petway to analyze the force with which basketball players jump (and land), and even to determine how much force an athlete is generating from each leg. Using a tool called a linear position transducer, Petway can measure how fast athletes are moving a prescribed load during weight-lifting exercises.

      “Instead of telling someone to do 90 percent of their squat max, we’re telling them to squat 200 kilos, and to move it at a rate above one meter per second,” says Petway. “So it’s more power- and velocity-driven than your traditional weight training.”

      The goal is to not only improve athlete’s performance, Petway says, but also to create training programs that minimize the chance of injury. Sometimes, that means deviating from well-worn sports cliches about “giving 110 percent” or “leaving it all on the court.”

      “There’s a misconception that doing more is always better,” Petway says. “One of my mentors would always say, ‘Sometimes you have to have the courage to do less.’ The most important thing for our athletes is being available for competition. We can use data analytics now to forecast the early onset of fatigue. If we see that their power output in the weight room is decreasing, we may need to intervene with rest before things get worse. It’s about using information to make more objective decisions.”

      The ability to create visuals from data, Petway says, has greatly enhanced his ability to communicate with athletes and other coaches about what he’s seeing in the numbers. “It’s a really powerful tool, being able to take a bunch of data points and show that things are trending up or down, along with the intervention we’re going to need to make based on what the data suggests,” he says.

      Ultimately, Petway notes, coaches are primarily interested in just one data point: wins and losses. But as more sports professionals see that data science can lead to more wins, he says, analytics will continue to gain a foothold in the industry. “If you can show that preparing a certain way leads to a higher likelihood that the team will win, that really speaks coaches’ language,” he says. “They just want to see results. And if data science can help deliver those results, they’re going to be bought in.” More

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      Caspar Hare, Georgia Perakis named associate deans of Social and Ethical Responsibilities of Computing

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      Caspar Hare and Georgia Perakis have been appointed the new associate deans of the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative in the MIT Stephen A. Schwarzman College of Computing. Their new roles will take effect on Sept. 1.

      “Infusing social and ethical aspects of computing in academic research and education is a critical component of the college mission,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “I look forward to working with Caspar and Georgia on continuing to develop and advance SERC and its reach across MIT. Their complementary backgrounds and their broad connections across MIT will be invaluable to this next chapter of SERC.”

      Caspar Hare

      Hare is a professor of philosophy in the Department of Linguistics and Philosophy. A member of the MIT faculty since 2003, his main interests are in ethics, metaphysics, and epistemology. The general theme of his recent work has been to bring ideas about practical rationality and metaphysics to bear on issues in normative ethics and epistemology. He is the author of two books: “On Myself, and Other, Less Important Subjects” (Princeton University Press 2009), about the metaphysics of perspective, and “The Limits of Kindness” (Oxford University Press 2013), about normative ethics.

      Georgia Perakis

      Perakis is the William F. Pounds Professor of Management and professor of operations research, statistics, and operations management at the MIT Sloan School of Management, where she has been a faculty member since 1998. She investigates the theory and practice of analytics and its role in operations problems and is particularly interested in how to solve complex and practical problems in pricing, revenue management, supply chains, health care, transportation, and energy applications, among other areas. Since 2019, she has been the co-director of the Operations Research Center, an interdepartmental PhD program that jointly reports to MIT Sloan and the MIT Schwarzman College of Computing, a role in which she will remain. Perakis will also assume an associate dean role at MIT Sloan in recognition of her leadership.

      Hare and Perakis succeed David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, and Julie Shah, the H.N. Slater Professor of Aeronautics and Astronautics, who will be stepping down from their roles at the conclusion of their three-year term on Aug. 31.

      “My deepest thanks to Dave and Julie for their tremendous leadership of SERC and contributions to the college as associate deans,” says Huttenlocher.

      SERC impact

      As the inaugural associate deans of SERC, Kaiser and Shah have been responsible for advancing a mission to incorporate humanist, social science, social responsibility, and civic perspectives into MIT’s teaching, research, and implementation of computing. In doing so, they have engaged dozens of faculty members and thousands of students from across MIT during these first three years of the initiative.

      They have brought together people from a broad array of disciplines to collaborate on crafting original materials such as active learning projects, homework assignments, and in-class demonstrations. A collection of these materials was recently published and is now freely available to the world via MIT OpenCourseWare.

      In February 2021, they launched the MIT Case Studies in Social and Ethical Responsibilities of Computing for undergraduate instruction across a range of classes and fields of study. The specially commissioned and peer-reviewed cases are based on original research and are brief by design. Three issues have been published to date and a fourth will be released later this summer. Kaiser will continue to oversee the successful new series as editor.

      Last year, 60 undergraduates, graduate students, and postdocs joined a community of SERC Scholars to help advance SERC efforts in the college. The scholars participate in unique opportunities throughout, such as the summer Experiential Ethics program. A multidisciplinary team of graduate students last winter worked with the instructors and teaching assistants of class 6.036 (Introduction to Machine Learning), MIT’s largest machine learning course, to infuse weekly labs with material covering ethical computing, data and model bias, and fairness in machine learning through SERC.

      Through efforts such as these, SERC has had a substantial impact at MIT and beyond. Over the course of their tenure, Kaiser and Shah have engaged about 80 faculty members, and more than 2,100 students took courses that included new SERC content in the last year alone. SERC’s reach extended well beyond engineering students, with about 500 exposed to SERC content through courses offered in the School of Humanities, Arts, and Social Sciences, the MIT Sloan School of Management, and the School of Architecture and Planning. More

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      Transforming the travel experience for the Hong Kong airport

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      MIT Hong Kong Innovation Node welcomed 33 students to its flagship program, MIT Entrepreneurship and Maker Skills Integrator (MEMSI). Designed to develop entrepreneurial prowess through exposure to industry-driven challenges, MIT students joined forces with Hong Kong peers in this two-week hybrid bootcamp, developing unique proposals for the Airport Authority of Hong Kong.

      Many airports across the world continue to be affected by the broader impact of Covid-19 with reduced air travel, prompting airlines to cut capacity. The result is a need for new business opportunities to propel economic development. For Hong Kong, the expansion toward non-aeronautical activities to boost regional consumption is therefore crucial, and included as part of the blueprint to transform the city’s airport into an airport city — characterized by capacity expansion, commercial developments, air cargo leadership, an autonomous transport system, connectivity to neighboring cities in mainland China, and evolution into a smart airport guided by sustainable practices. To enhance the customer experience, a key focus is capturing business opportunities at the nexus of digital and physical interactions. 

      These challenges “bring ideas and talent together to tackle real-world problems in the areas of digital service creation for the airport and engaging regional customers to experience the new airport city,” says Charles Sodini, the LeBel Professor of Electrical Engineering at MIT and faculty director at the Node. 

      The new travel standard

      Businesses are exploring new digital technologies, both to drive bookings and to facilitate safe travel. Developments such as Hong Kong airport’s Flight Token, a biometric technology using facial recognition to enable contactless check-ins and boarding at airports, unlock enormous potential that speeds up the departure journey of passengers. Seamless virtual experiences are not going to disappear.

      “What we may see could be a strong rebounce especially for travelers after the travel ban lifts … an opportunity to make travel easier, flying as simple as riding the bus,” says Chris Au Young, general manager of smart airport and general manager of data analytics at the Airport Authority of Hong Kong. 

      The passenger experience of the future will be “enabled by mobile technology, internet of things, and digital platforms,” he explains, adding that in the aviation community, “international organizations have already stipulated that biometric technology will be the new standard for the future … the next question is how this can be connected across airports.”  

      This extends further beyond travel, where Au Young illustrates, “If you go to a concert at Asia World Expo, which is the airport’s new arena in the future, you might just simply show your face rather than queue up in a long line waiting to show your tickets.”

      Accelerating the learning curve with industry support

      Working closely with industry mentors involved in the airport city’s development, students dived deep into discussions on the future of adapted travel, interviewed and surveyed travelers, and plowed through a range of airport data to uncover business insights.

      “With the large amount of data provided, my teammates and I worked hard to identify modeling opportunities that were both theoretically feasible and valuable in a business sense,” says Sean Mann, a junior at MIT studying computer science.

      Mann and his team applied geolocation data to inform machine learning predictions on a passenger’s journey once they enter the airside area. Coupled with biometric technology, passengers can receive personalized recommendations with improved accuracy via the airport’s bespoke passenger app, powered by data collected through thousands of iBeacons dispersed across the vicinity. Armed with these insights, the aim is to enhance the user experience by driving meaningful footfall to retail shops, restaurants, and other airport amenities.

      The support of industry partners inspired his team “with their deep understanding of the aviation industry,” he added. “In a short period of two weeks, we built a proof-of-concept and a rudimentary business plan — the latter of which was very new to me.”

      Collaborating across time zones, Rumen Dangovski, a PhD candidate in electrical engineering and computer science at MIT, joined MEMSI from his home in Bulgaria. For him, learning “how to continually revisit ideas to discover important problems and meaningful solutions for a large and complex real-world system” was a key takeaway. The iterative process helped his team overcome the obstacle of narrowing down the scope of their proposal, with the help of industry mentors and advisors. 

      “Without the feedback from industry partners, we would not have been able to formulate a concrete solution that is actually helpful to the airport,” says Dangovski.  

      Beyond valuable mentorship, he adds, “there was incredible energy in our team, consisting of diverse talent, grit, discipline and organization. I was positively surprised how MEMSI can form quickly and give continual support to our team. The overall experience was very fun.“

      A sustainable future

      Mrigi Munjal, a PhD candidate studying materials science and engineering at MIT, had just taken a long-haul flight from Boston to Delhi prior to the program, and “was beginning to fully appreciate the scale of carbon emissions from aviation.” For her, “that one journey basically overshadowed all of my conscious pro-sustainability lifestyle changes,” she says.

      Knowing that international flights constitute the largest part of an individual’s carbon footprint, Munjal and her team wanted “to make flying more sustainable with an idea that is economically viable for all of the stakeholders involved.” 

      They proposed a carbon offset API that integrates into an airline’s ticket payment system, empowering individuals to take action to offset their carbon footprint, track their personal carbon history, and pick and monitor green projects. The advocacy extends to a digital display of interactive art featured in physical installations across the airport city. The intent is to raise community awareness about one’s impact on the environment and making carbon offsetting accessible. 

      Shaping the travel narrative

      Six teams of students created innovative solutions for the Hong Kong airport which they presented in hybrid format to a panel of judges on Showcase Day. The diverse ideas included an app-based airport retail recommendations supported by iBeacons; a platform that empowers customers to offset their carbon footprint; an app that connects fellow travelers for social and incentive-driven retail experiences; a travel membership exchange platform offering added flexibility to earn and redeem loyalty rewards; an interactive and gamified location-based retail experience using augmented reality; and a digital companion avatar to increase adoption of the airport’s Flight Token and improve airside passenger experience.

      Among the judges was Julian Lee ’97, former president of the MIT Club of Hong Kong and current executive director of finance at the Airport Authority of Hong Kong, who commended the students for demonstrably having “worked very thoroughly and thinking through the specific challenges,” addressing the real pain points that the airport is experiencing.

      “The ideas were very thoughtful and very unique to us. Some of you defined transit passengers as a sub-segment of the market that works. It only happens at the airport and you’ve been able to leverage this transit time in between,” remarked Lee. 

      Strong solutions include an implementation plan to see a path for execution and a viable future. Among the solutions proposed, Au Young was impressed by teams for “paying a lot of attention to the business model … a very important aspect in all the ideas generated.”  

      Addressing the students, Au Young says, “What we love is the way you reinvent the airport business and partnerships, presenting a new way of attracting people to engage more in new services and experiences — not just returning for a flight or just shopping with us, but innovating beyond the airport and using emerging technologies, using location data, using the retailer’s capability and adding some social activities in your solutions.”

      Despite today’s rapidly evolving travel industry, what remains unchanged is a focus on the customer. In the end, “it’s still about the passengers,” added Au Young.  More

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      Unlocking new doors to artificial intelligence

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      Artificial intelligence research is constantly developing new hypotheses that have the potential to benefit society and industry; however, sometimes these benefits are not fully realized due to a lack of engineering tools. To help bridge this gap, graduate students in the MIT Department of Electrical Engineering and Computer Science’s 6-A Master of Engineering (MEng) Thesis Program work with some of the most innovative companies in the world and collaborate on cutting-edge projects, while contributing to and completing their MEng thesis.

      During a portion of the last year, four 6-A MEng students teamed up and completed an internship with IBM Research’s advanced prototyping team through the MIT-IBM Watson AI Lab on AI projects, often developing web applications to solve a real-world issue or business use cases. Here, the students worked alongside AI engineers, user experience engineers, full-stack researchers, and generalists to accommodate project requests and receive thesis advice, says Lee Martie, IBM research staff member and 6-A manager. The students’ projects ranged from generating synthetic data to allow for privacy-sensitive data analysis to using computer vision to identify actions in video that allows for monitoring human safety and tracking build progress on a construction site.

      “I appreciated all of the expertise from the team and the feedback,” says 6-A graduate Violetta Jusiega ’21, who participated in the program. “I think that working in industry gives the lens of making sure that the project’s needs are satisfied and [provides the opportunity] to ground research and make sure that it is helpful for some use case in the future.”

      Jusiega’s research intersected the fields of computer vision and design to focus on data visualization and user interfaces for the medical field. Working with IBM, she built an application programming interface (API) that let clinicians interact with a medical treatment strategy AI model, which was deployed in the cloud. Her interface provided a medical decision tree, as well as some prescribed treatment plans. After receiving feedback on her design from physicians at a local hospital, Jusiega developed iterations of the API and how the results where displayed, visually, so that it would be user-friendly and understandable for clinicians, who don’t usually code. She says that, “these tools are often not acquired into the field because they lack some of these API principles which become more important in an industry where everything is already very fast paced, so there’s little time to incorporate a new technology.” But this project might eventually allow for industry deployment. “I think this application has a bunch of potential, whether it does get picked up by clinicians or whether it’s simply used in research. It’s very promising and very exciting to see how technology can help us modify, or I can improve, the health-care field to be even more custom-tailored towards patients and giving them the best care possible,” she says.

      Another 6-A graduate student, Spencer Compton, was also considering aiding professionals to make more informed decisions, for use in settings including health care, but he was tackling it from a causal perspective. When given a set of related variables, Compton was investigating if there was a way to determine not just correlation, but the cause-and-effect relationship between them (the direction of the interaction) from the data alone. For this, he and his collaborators from IBM Research and Purdue University turned to a field of math called information theory. With the goal of designing an algorithm to learn complex networks of causal relationships, Compton used ideas relating to entropy, the randomness in a system, to help determine if a causal relationship is present and how variables might be interacting. “When judging an explanation, people often default to Occam’s razor” says Compton. “We’re more inclined to believe a simpler explanation than a more complex one.” In many cases, he says, it seemed to perform well. For instance, they were able to consider variables such as lung cancer, pollution, and X-ray findings. He was pleased that his research allowed him to help create a framework of “entropic causal inference” that could aid in safe and smart decisions in the future, in a satisfying way. “The math is really surprisingly deep, interesting, and complex,” says Compton. “We’re basically asking, ‘when is the simplest explanation correct?’ but as a math question.”

      Determining relationships within data can sometimes require large volumes of it to suss out patterns, but for data that may contain sensitive information, this may not be available. For her master’s work, Ivy Huang worked with IBM Research to generate synthetic tabular data using a natural language processing tool called a transformer model, which can learn and predict future values from past values. Trained on real data, the model can produce new data with similar patterns, properties, and relationships without restrictions like privacy, availability, and access that might come with real data in financial transactions and electronic medical records. Further, she created an API and deployed the model in an IBM cluster, which allowed users increased access to the model and abilities to query it without compromising the original data.

      Working with the advanced prototyping team, MEng candidate Brandon Perez also considered how to gather and investigate data with restrictions, but in his case it was to use computer vision frameworks, centered on an action recognition model, to identify construction site happenings. The team based their work on the Moments in Time dataset, which contains over a million three-second video clips with about 300 attached classification labels, and has performed well during AI training. However, the group needed more construction-based video data. For this, they used YouTube-8M. Perez built a framework for testing and fine-tuning existing object detection models and action recognition models that could plug into an automatic spatial and temporal localization tool — how they would identify and label particular actions in a video timeline. “I was satisfied that I was able to explore what made me curious, and I was grateful for the autonomy that I was given with this project,” says Perez. “I felt like I was always supported, and my mentor was a great support to the project.”

      “The kind of collaborations that we have seen between our MEng students and IBM researchers are exactly what the 6-A MEng Thesis program at MIT is all about,” says Tomas Palacios, professor of electrical engineering and faculty director of the MIT 6-A MEng Thesis program. “For more than 100 years, 6-A has been connecting MIT students with industry to solve together some of the most important problems in the world.” More

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      Physics and the machine-learning “black box”

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      Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the “black box” problem, through a combination of data science and physics-based engineering.

      In class 2.C161 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

      “I wanted to take 2.C161 because machine-learning models are usually a “black box,” but this class taught us how to construct a system model that is informed by physics so we can peek inside,” explains Crystal Owens, a mechanical engineering graduate student who took the course in spring 2021.

      As chair of the Committee on the Strategic Integration of Data Science into Mechanical Engineering, Barbastathis has had many conversations with mechanical engineering students, researchers, and faculty to better understand the challenges and successes they’ve had using machine learning in their work.

      “One comment we heard frequently was that these colleagues can see the value of data science methods for problems they are facing in their mechanical engineering-centric research; yet they are lacking the tools to make the most out of it,” says Barbastathis. “Mechanical, civil, electrical, and other types of engineers want a fundamental understanding of data principles without having to convert themselves to being full-time data scientists or AI researchers.”

      Additionally, as mechanical engineering students move on from MIT to their careers, many will need to manage data scientists on their teams someday. Barbastathis hopes to set these students up for success with class 2.C161.

      Bridging MechE and the MIT Schwartzman College of Computing

      Class 2.C161 is part of the MIT Schwartzman College of Computing “Computing Core.” The goal of these classes is to connect data science and physics-based engineering disciplines, like mechanical engineering. Students take the course alongside 6.C402 (Modeling with Machine Learning: from Algorithms to Applications), taught by professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola.

      The two classes are taught concurrently during the semester, exposing students to both fundamentals in machine learning and domain-specific applications in mechanical engineering.

      In 2.C161, Barbastathis highlights how complementary physics-based engineering and data science are. Physical laws present a number of ambiguities and unknowns, ranging from temperature and humidity to electromagnetic forces. Data science can be used to predict these physical phenomena. Meanwhile, having an understanding of physical systems helps ensure the resulting output of an algorithm is accurate and explainable.

      “What’s needed is a deeper combined understanding of the associated physical phenomena and the principles of data science, machine learning in particular, to close the gap,” adds Barbastathis. “By combining data with physical principles, the new revolution in physics-based engineering is relatively immune to the “black box” problem facing other types of machine learning.”

      Equipped with a working knowledge of machine-learning topics covered in class 6.C402 and a deeper understanding of how to pair data science with physics, students are charged with developing a final project that solves for an actual physical system.

      Developing solutions for real-world physical systems

      For their final project, students in 2.C161 are asked to identify a real-world problem that requires data science to address the ambiguity inherent in physical systems. After obtaining all relevant data, students are asked to select a machine-learning method, implement their chosen solution, and present and critique the results.

      Topics this past semester ranged from weather forecasting to the flow of gas in combustion engines, with two student teams drawing inspiration from the ongoing Covid-19 pandemic.

      Owens and her teammates, fellow graduate students Arun Krishnadas and Joshua David John Rathinaraj, set out to develop a model for the Covid-19 vaccine rollout.

      “We developed a method of combining a neural network with a susceptible-infected-recovered (SIR) epidemiological model to create a physics-informed prediction system for the spread of Covid-19 after vaccinations started,” explains Owens.

      The team accounted for various unknowns including population mobility, weather, and political climate. This combined approach resulted in a prediction of Covid-19’s spread during the vaccine rollout that was more reliable than using either the SIR model or a neural network alone.

      Another team, including graduate student Yiwen Hu, developed a model to predict mutation rates in Covid-19, a topic that became all too pertinent as the delta variant began its global spread.

      “We used machine learning to predict the time-series-based mutation rate of Covid-19, and then incorporated that as an independent parameter into the prediction of pandemic dynamics to see if it could help us better predict the trend of the Covid-19 pandemic,” says Hu.

      Hu, who had previously conducted research into how vibrations on coronavirus protein spikes affect infection rates, hopes to apply the physics-based machine-learning approaches he learned in 2.C161 to his research on de novo protein design.

      Whatever the physical system students addressed in their final projects, Barbastathis was careful to stress one unifying goal: the need to assess ethical implications in data science. While more traditional computing methods like face or voice recognition have proven to be rife with ethical issues, there is an opportunity to combine physical systems with machine learning in a fair, ethical way.

      “We must ensure that collection and use of data are carried out equitably and inclusively, respecting the diversity in our society and avoiding well-known problems that computer scientists in the past have run into,” says Barbastathis.

      Barbastathis hopes that by encouraging mechanical engineering students to be both ethics-literate and well-versed in data science, they can move on to develop reliable, ethically sound solutions and predictions for physical-based engineering challenges. More

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      Q&A: Cathy Wu on developing algorithms to safely integrate robots into our world

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      Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Data, Systems, and Society. As an undergraduate, Wu won MIT’s toughest robotics competition, and as a graduate student took the University of California at Berkeley’s first-ever course on deep reinforcement learning. Now back at MIT, she’s working to improve the flow of robots in Amazon warehouses under the Science Hub, a new collaboration between the tech giant and the MIT Schwarzman College of Computing. Outside of the lab and classroom, Wu can be found running, drawing, pouring lattes at home, and watching YouTube videos on math and infrastructure via 3Blue1Brown and Practical Engineering. She recently took a break from all of that to talk about her work.

      Q: What put you on the path to robotics and self-driving cars?

      A: My parents always wanted a doctor in the family. However, I’m bad at following instructions and became the wrong kind of doctor! Inspired by my physics and computer science classes in high school, I decided to study engineering. I wanted to help as many people as a medical doctor could.

      At MIT, I looked for applications in energy, education, and agriculture, but the self-driving car was the first to grab me. It has yet to let go! Ninety-four percent of serious car crashes are caused by human error and could potentially be prevented by self-driving cars. Autonomous vehicles could also ease traffic congestion, save energy, and improve mobility.

      I first learned about self-driving cars from Seth Teller during his guest lecture for the course Mobile Autonomous Systems Lab (MASLAB), in which MIT undergraduates compete to build the best full-functioning robot from scratch. Our ball-fetching bot, Putzputz, won first place. From there, I took more classes in machine learning, computer vision, and transportation, and joined Teller’s lab. I also competed in several mobility-related hackathons, including one sponsored by Hubway, now known as Blue Bike.

      Q: You’ve explored ways to help humans and autonomous vehicles interact more smoothly. What makes this problem so hard?

      A: Both systems are highly complex, and our classical modeling tools are woefully insufficient. Integrating autonomous vehicles into our existing mobility systems is a huge undertaking. For example, we don’t know whether autonomous vehicles will cut energy use by 40 percent, or double it. We need more powerful tools to cut through the uncertainty. My PhD thesis at Berkeley tried to do this. I developed scalable optimization methods in the areas of robot control, state estimation, and system design. These methods could help decision-makers anticipate future scenarios and design better systems to accommodate both humans and robots.

      Q: How is deep reinforcement learning, combining deep and reinforcement learning algorithms, changing robotics?

      A: I took John Schulman and Pieter Abbeel’s reinforcement learning class at Berkeley in 2015 shortly after Deepmind published their breakthrough paper in Nature. They had trained an agent via deep learning and reinforcement learning to play “Space Invaders” and a suite of Atari games at superhuman levels. That created quite some buzz. A year later, I started to incorporate reinforcement learning into problems involving mixed traffic systems, in which only some cars are automated. I realized that classical control techniques couldn’t handle the complex nonlinear control problems I was formulating.

      Deep RL is now mainstream but it’s by no means pervasive in robotics, which still relies heavily on classical model-based control and planning methods. Deep learning continues to be important for processing raw sensor data like camera images and radio waves, and reinforcement learning is gradually being incorporated. I see traffic systems as gigantic multi-robot systems. I’m excited for an upcoming collaboration with Utah’s Department of Transportation to apply reinforcement learning to coordinate cars with traffic signals, reducing congestion and thus carbon emissions.

      Q: You’ve talked about the MIT course, 6.007 (Signals and Systems), and its impact on you. What about it spoke to you?

      A: The mindset. That problems that look messy can be analyzed with common, and sometimes simple, tools. Signals are transformed by systems in various ways, but what do these abstract terms mean, anyway? A mechanical system can take a signal like gears turning at some speed and transform it into a lever turning at another speed. A digital system can take binary digits and turn them into other binary digits or a string of letters or an image. Financial systems can take news and transform it via millions of trading decisions into stock prices. People take in signals every day through advertisements, job offers, gossip, and so on, and translate them into actions that in turn influence society and other people. This humble class on signals and systems linked mechanical, digital, and societal systems and showed me how foundational tools can cut through the noise.

      Q: In your project with Amazon you’re training warehouse robots to pick up, sort, and deliver goods. What are the technical challenges?

      A: This project involves assigning robots to a given task and routing them there. [Professor] Cynthia Barnhart’s team is focused on task assignment, and mine, on path planning. Both problems are considered combinatorial optimization problems because the solution involves a combination of choices. As the number of tasks and robots increases, the number of possible solutions grows exponentially. It’s called the curse of dimensionality. Both problems are what we call NP Hard; there may not be an efficient algorithm to solve them. Our goal is to devise a shortcut.

      Routing a single robot for a single task isn’t difficult. It’s like using Google Maps to find the shortest path home. It can be solved efficiently with several algorithms, including Dijkstra’s. But warehouses resemble small cities with hundreds of robots. When traffic jams occur, customers can’t get their packages as quickly. Our goal is to develop algorithms that find the most efficient paths for all of the robots.

      Q: Are there other applications?

      A: Yes. The algorithms we test in Amazon warehouses might one day help to ease congestion in real cities. Other potential applications include controlling planes on runways, swarms of drones in the air, and even characters in video games. These algorithms could also be used for other robotic planning tasks like scheduling and routing.

      Q: AI is evolving rapidly. Where do you hope to see the big breakthroughs coming?

      A: I’d like to see deep learning and deep RL used to solve societal problems involving mobility, infrastructure, social media, health care, and education. Deep RL now has a toehold in robotics and industrial applications like chip design, but we still need to be careful in applying it to systems with humans in the loop. Ultimately, we want to design systems for people. Currently, we simply don’t have the right tools.

      Q: What worries you most about AI taking on more and more specialized tasks?

      A: AI has the potential for tremendous good, but it could also help to accelerate the widening gap between the haves and the have-nots. Our political and regulatory systems could help to integrate AI into society and minimize job losses and income inequality, but I worry that they’re not equipped yet to handle the firehose of AI.

      Q: What’s the last great book you read?

      A: “How to Avoid a Climate Disaster,” by Bill Gates. I absolutely loved the way that Gates was able to take an overwhelmingly complex topic and distill it down into words that everyone can understand. His optimism inspires me to keep pushing on applications of AI and robotics to help avoid a climate disaster. More

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      in Data Management & Statistics

      Studying learner engagement during the Covid-19 pandemic

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      While massive open online classes (MOOCs) have been a significant trend in higher education for many years now, they have gained a new level of attention during the Covid-19 pandemic. Open online courses became a critical resource for a wide audience of new learners during the first stages of the pandemic — including students whose academic programs had shifted online, teachers seeking online resources, and individuals suddenly facing lockdown or unemployment and looking to build new skills.

      Mary Ellen Wiltrout, director of online and blended learning initiatives and lecturer in digital learning in the Department of Biology, and Virginia “Katie” Blackwell, currently an MIT PhD student in biology, published a paper this summer in the European MOOC Stakeholder Summit (EMOOCs 2021) conference proceedings evaluating data for the online course 7.00x (Introduction to Biology). Their research objective was to better understand whether the shift to online learning that occurred during the pandemic led to increased learner engagement in the course.Blackwell participated in this research as part of the Bernard S. and Sophie G. Gould MIT Summer Research Program (MSRP) in Biology, during the uniquely remote MSRPx-Biology 2020 student cohort. She collaborated on the project while working toward her bachelor’s degree in biochemistry and molecular biology from the University of Texas at Dallas, and collaborated on the research while in Texas. She has since applied and been accepted into MIT’s PhD program in biology.

      “MSRP Biology was a transformative experience for me. I learned a lot about the nature of research and the MIT community in a very short period of time and loved every second of the program. Without MSRP, I would never have even considered applying to MIT for my PhD. After MSRP and working with Mary Ellen, MIT biology became my first-choice program and I felt like I had a shot at getting in,” says Blackwell.

      Play video

      Many MOOC platforms experienced increased website traffic in 2020, with 30 new MOOC-based degrees and more than 60 million new learners.

      “We find that the tremendous, lifelong learning opportunities that MOOCs provide are even more important and sought-after when traditional education is disrupted. During the pandemic, people had to be at home more often, and some faced unemployment requiring a career transition,” says Wiltrout.

      Wiltrout and Blackwell wanted to build a deeper understanding of learner profiles rather than looking exclusively at enrollments. They looked at all available data, including: enrollment demographics (i.e., country and “.edu” participants); proportion of learners engaged with videos, problems, and forums; number of individual engagement events with videos, problems, and forums; verification and performance; and the course “track” level — including auditing (for free) and verified (paying and receiving access to additional course content, including access to a comprehensive competency exam). They analyzed data in these areas from five runs of 7.00x in this study, including three pre-pandemic runs of April, July, and November 2019 and two pandemic runs of March and July 2020. 

      The March 2020 run had the same count of verified-track participants as all three pre-pandemic runs combined. The July 2020 run enrolled nearly as many verified-track participants as the March 2020 run. Wiltrout says that introductory biology content may have attracted great attention during the early days and months of the Covid-19 pandemic, as people may have had a new (or renewed) interest in learning about (or reviewing) viruses, RNA, the inner workings of cells, and more.

      Wiltrout and Blackwell found that the enrollment count for the March 2020 run of the course increased at almost triple the rate of the three pre-pandemic runs. During the early days of March 2020, the enrollment metrics appeared similar to enrollment metrics for the April 2019 run — both in rate and count — but the enrollment rate increased sharply around March 15, 2020. The July 2020 run began with more than twice as many learners already enrolled by the first day of the course, but continued with half the enrollment rate of the March 2020 course. In terms of learner demographics, during the pandemic, there was a higher proportion of learners with .edu addresses, indicating that MOOCs were often used by students enrolled in other schools. 

      Viewings of course videos increased at the beginning of the pandemic. During the March 2020 run, both verified-track and certified participants viewed far more unique videos during March 2020 than in the pre-pandemic runs of the course; even auditor-track learners — not aiming for certification — still viewed all videos offered. During the July 2020 run, however, both verified-track and certified participants viewed far fewer unique videos than during all prior runs. The proportion of participants who viewed at least one video decreased in the July 2020 run to 53 percent, from a mean of 64 percent in prior runs. Blackwell and Wiltrout say that this decrease — as well as the overall dip in participation in July 2020 — might be attributed to shifting circumstances for learners that allowed for less time to watch videos and participate in the course, as well as some fatigue from the extra screen time.

      The study found that 4.4 percent of March 2020 participants and 4.5 percent of July 2020 participants engaged through forum posting — which was 1.4 to 3.3 times higher than pre-pandemic proportions of forum posting. The increase in forum engagement may point to a desire for community engagement during a time when many were isolated and sheltering in place.

      “Through the day-to-day work of my research team and also through the engagement of the learners in 7.00x, we can see that there is great potential for meaningful connections in remote experiences,” says Wiltrout. “An increase in participation for an online course may not always remain at the same high level, in the long term, but overall, we’re continuing to see an increase in the number of MOOCs and other online programs offered by all universities and institutions, as well as an increase in online learners.” More

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      in Data Management & Statistics

      “AI for Impact” lives up to its name

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      For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy.

      Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities. The app and related software eventually got spun out into the nonprofit PathCheck Foundation, which today engages with public health entities and is providing exposure notifications in Guam, Cyprus, Hawaii, Minnesota, Alabama, and Louisiana.

      The formation of Safe Paths demonstrates the special sense among MIT researchers that “we can launch something that can help people around the world,” notes Media Lab Associate Professor Ramesh Raskar, who teaches the class together with Media Lab Professor Alex “Sandy” Pentland and Media Lab Lecturer Joost Bonsen. “To have that kind of passion and ambition — but also the confidence that what you create here can actually be deployed globally — is kind of amazing.”

      AI for Impact, created by Pentland, began meeting two decades ago under the course name Development Ventures, and has nurtured multiple thriving businesses. Examples of class ventures that Pentland incubated or co-founded include Dimagi, Cogito, Ginger, Prosperia, and Sanergy.

      The aim-high challenge posed to each class is to come up with a business plan that touches a billion people, and it can’t all be in one country, Pentland explains. Not every class effort becomes a business, “but 20 percent to 30 percent of students start something, which is great for an entrepreneur class,” says Pentland.

      Opportunities for Impact

      The numbers behind Dimagi, for instance, are striking. Its core product CommCare has helped front-line health workers provide care for more than 400 million people in more than 130 countries around the world. When it comes to maternal and child care, Dimagi’s platform has registered one in every 110 pregnancies worldwide. This past year, several governments around the world deployed CommCare applications for Covid-19 response — from Sierra Leone and Somalia to New York and Colorado.

      Spinoffs like Cogito, Prosperia, and Ginger have likewise grown into highly successful companies. Cogito helps a million people a day gain access to the health care they need; Prosperia helps manage social support payments to 80 million people in Latin America; and Ginger handles mental health services for over 1 million people.

      The passion behind these and other class ventures points to a central idea of the class, Pentland notes: MIT students are often looking for ways to build entrepreneurial businesses that enable positive social change.

      During the spring 2021 class, for example, a number of promising student projects included tools to help residents of poor communities transition to owning their homes rather than renting, and to take better control of their community health.

      “It’s clear that the people who are graduating from here want to do something significant with their lives … they want to have an impact on their world,” Pentland says. “This class enables them to meet other people who are interested in doing the same thing, and offers them some help in starting a company to do it.”

      Many of the students who join the class come in with a broad set of interests. Guest lectures, case studies of other social entrepreneurship projects, and an introduction to a broad ecosystem of expertise and funding, then helps students to refine their general ideas into specific and viable projects.

      A path toward confronting a pandemic 

      Raskar began co-teaching the class in 2019, and brought a “Big AI” focus to the Development Ventures class, inspired by an AI for Impact team he had set up at his former employer, Facebook. “What I realized is that companies like Google or Facebook or Amazon actually have enough data about all of us that they can solve major problems in our society — climate, transportation, health, and so on,” he says. “This is something we should think about more seriously: how to use AI and data for positive social impact, while protecting privacy.”

      Early into the spring 2020 class, as students were beginning to consider their own projects, Raskar approached the class about the emerging coronavirus outbreak. Students like Kristen Vilcans recognized the urgency, and the opportunity. She and 10 other students joined forces to work on a project that would focus on Covid-19.

      “Students felt empowered to do something to help tackle the spread of this alarming new virus,” Raskar recalls. “They immediately began to develop data- and AI-based solutions to one of the most critical pieces of addressing a pandemic: halting the chain of infections. They created and launched one of the first digital contact tracing and exposure notification solutions in the U.S., developing an early alert system that engaged the public and protected privacy.” 

      Raskar looks back on the moment when a core group of students coalesced into a team. “It was very rare for a significant part of the class to just come together saying, ‘let’s do this, right away.’ It became as much a movement as a venture.”

      Group discussions soon began to center around an open-source, privacy-first digital set of tools for Covid-19 contact tracing. For the next two weeks, right up to the campus shutdown in March 2020, the team took over two adjacent conference rooms in the Media Lab, and started a Slack messaging channel devoted to the project. As the team members reached out to an ever-wider circle of friends, colleagues, and mentors, the number of participants grew to nearly 1,600 people, coming together virtually from all corners of the world.

      Kaushal Jain, a Harvard Business School student who had cross-registered for the spring 2020 class to get to know the MIT ecosystem, was also an early participant in Safe Paths. He wrote up an initial plan for the venture and began working with external organizations to figure out how to structure it into a nonprofit company. Jain eventually became the project’s lead for funding and partnerships.

      Vilcans, a graduate student in system design and management, served as Safe Paths’ communications lead through July 2020, while still working a part-time job at Draper Laboratory and taking classes.

      “There are these moments when you want to dive in, you want to contribute and you want to work nonstop,” she says, adding that the experience was also a wake-up call on how to manage burnout, and how to balance what you need as a person while contributing to a high-impact team. “That’s important to understand as a leader for the future.”

      MIT recognized Vilcan’s contributions later that year with the 2020 SDM Student Award for Leadership, Innovation, and Systems Thinking. 

      Jain, too, says the class gave him more than he could have expected.

      “I made strong friendships with like-minded people from very different backgrounds,” he says. “One key thing that I learned was to be flexible about the kind of work you want to do. Be open and see if there’s an opportunity, either through crisis or through something that you believe could really change a lot of things in the world. And then just go for it.” More