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    Unlocking new doors to artificial intelligence

    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”

    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

    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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    Studying learner engagement during the Covid-19 pandemic

    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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    “AI for Impact” lives up to its name

    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

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    “To make even the smallest contribution to improving my country would be my dream”

    Thailand has become an economic leader in Southeast Asia in recent decades, but while the country has rapidly industrialized, many Thai citizens have been left behind. As a child growing up in Bangkok, Pavarin Bhandtivej would watch the news and wonder why families in the nearby countryside had next to nothing. He aspired to become a policy researcher and create beneficial change.

    But Bhandtivej knew his goal wouldn’t be easy. He was born with a visual impairment, making it challenging for him to see, read, and navigate. This meant he had to work twice as hard in school to succeed. It took achieving the highest grades for Bhandtivej to break through stigmas and have his talents recognized. Still, he persevered, with a determination to uplift others. “I would return to that initial motivation I had as a kid. For me, to make even the smallest contribution to improving my country would be my dream,” he says.

    “When I would face these obstacles, I would tell myself that struggling people are waiting for someone to design policies for them to have better lives. And that person could be me. I cannot fall here in front of these obstacles. I must stay motivated and move on.”

    Bhandtivej completed his undergraduate degree in economics at Thailand’s top college, Chulalongkorn University. His classes introduced him to many debates about development policy, such as universal basic income. During one debate, after both sides made compelling arguments about how to alleviate poverty, Bhandtivej realized there was no clear winner. “A question came to my mind: Who’s right?” he says. “In terms of theory, both sides were correct. But how could we know what approach would work in the real world?”

    A new approach to higher education

    The search for those answers would lead Bhandtivej to become interested in data analysis. He began investigating online courses, eventually finding the MIT MicroMasters Program in Data, Economics, and Development Policy (DEDP), which was created by MIT’s Department of Economics and the Abdul Latif Jameel Poverty Action Lab (J-PAL). The program requires learners to complete five online courses that teach quantitative methods for evaluating social programs, leading to a MicroMasters credential. Students that pass the courses’ proctored exams are then also eligible to apply for a full-time, accelerated, on-campus master’s program at MIT, led by professors Esther Duflo, Abhijit Banerjee, and Benjamin Olken.

    The program’s mission to make higher education more accessible worked well for Bhandtivej. He studied tirelessly, listening and relistening to online lectures and pausing to scrutinize equations. By the end, his efforts paid off — Bhandtivej was the MicroMasters program’s top scorer. He was soon admitted into the second cohort of the highly selective DEDP master’s program.

    “You can imagine how time-consuming it was to use text-to-speech to get through a 30-page reading with numerous equations, tables, and graphs,” he explains. “Luckily, Disability and Access Services provided accommodations to timed exams and I was able to push through.”   

    In the gap year before the master’s program began, Bhandtivej returned to Chulalongkorn University as a research assistant with Professor Thanyaporn Chankrajang. He began applying his newfound quantitative skills to study the impacts of climate change in Thailand. His contributions helped uncover how rising temperatures and irregular rainfall are leading to reduced rice crop yields. “Thailand is the world’s second largest exporter of rice, and the vast majority of Thais rely heavily on rice for its nutritional and commercial value. We need more data to encourage leaders to act now,” says Bhandtivej. “As a Buddhist, it was meaningful to be part of generating this evidence, as I am always concerned about my impact on other humans and sentient beings.”

    Staying true to his mission

    Now pursuing his master’s on campus, Bhandtivej is taking courses like 14.320 (Econometric Data Science) and studying how to design, conduct, and analyze empirical studies. “The professors I’ve had have opened a whole new world for me,” says Bhandtivej. “They’ve inspired me to see how we can take rigorous scientific practices and apply them to make informed policy decisions. We can do more than rely on theories.”

    The final portion of the program requires a summer capstone experience, which Bhandtivej is using to work at Innovations for Poverty Action. He has recently begun to analyze how remote learning interventions in Bangladesh have performed since Covid-19. Many teachers are concerned, since disruptions in childhood education can lead to intergenerational poverty. “We have tried interventions that connect students with teachers, provide discounted data packages, and send information on where to access adaptive learning technologies and other remote learning resources,” he says. “It will be interesting to see the results. This is a truly urgent topic, as I don’t believe Covid-19 will be the last pandemic of our lifetime.”

    Enhancing education has always been one of Bhandtivej’s priority interests. He sees education as the gateway that brings a person’s innate talent to light. “There is a misconception in many developing countries that disabled people cannot learn, which is untrue,” says Bhandtivej. “Education provides a critical signal to future employers and overall society that we can work and perform just as well, as long as we have appropriate accommodations.”

    In the future, Bhandtivej plans on returning to Thailand to continue his journey as a policy researcher. While he has many issues he would like to tackle, his true purpose still lies in doing work that makes a positive impact on people’s lives. “My hope is that my story encourages people to think of not only what they are capable of achieving themselves, but also what they can do for others.”

    “You may think you are just a small creature on a large planet. That you have just a tiny role to play. But I think — even if we are just a small part — whatever we can do to make life better for our communities, for our country, for our planet … it’s worth it.” More