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    Joining the battle against health care bias

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    Medical researchers are awash in a tsunami of clinical data. But we need major changes in how we gather, share, and apply this data to bring its benefits to all, says Leo Anthony Celi, principal research scientist at the MIT Laboratory for Computational Physiology (LCP). 

    One key change is to make clinical data of all kinds openly available, with the proper privacy safeguards, says Celi, a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC) in Boston. Another key is to fully exploit these open data with multidisciplinary collaborations among clinicians, academic investigators, and industry. A third key is to focus on the varying needs of populations across every country, and to empower the experts there to drive advances in treatment, says Celi, who is also an associate professor at Harvard Medical School. 

    In all of this work, researchers must actively seek to overcome the perennial problem of bias in understanding and applying medical knowledge. This deeply damaging problem is only heightened with the massive onslaught of machine learning and other artificial intelligence technologies. “Computers will pick up all our unconscious, implicit biases when we make decisions,” Celi warns.

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    Sharing medical data 

    Founded by the LCP, the MIT Critical Data consortium builds communities across disciplines to leverage the data that are routinely collected in the process of ICU care to understand health and disease better. “We connect people and align incentives,” Celi says. “In order to advance, hospitals need to work with universities, who need to work with industry partners, who need access to clinicians and data.” 

    The consortium’s flagship project is the MIMIC (medical information marked for intensive care) ICU database built at BIDMC. With about 35,000 users around the world, the MIMIC cohort is the most widely analyzed in critical care medicine. 

    International collaborations such as MIMIC highlight one of the biggest obstacles in health care: most clinical research is performed in rich countries, typically with most clinical trial participants being white males. “The findings of these trials are translated into treatment recommendations for every patient around the world,” says Celi. “We think that this is a major contributor to the sub-optimal outcomes that we see in the treatment of all sorts of diseases in Africa, in Asia, in Latin America.” 

    To fix this problem, “groups who are disproportionately burdened by disease should be setting the research agenda,” Celi says. 

    That’s the rule in the “datathons” (health hackathons) that MIT Critical Data has organized in more than two dozen countries, which apply the latest data science techniques to real-world health data. At the datathons, MIT students and faculty both learn from local experts and share their own skill sets. Many of these several-day events are sponsored by the MIT Industrial Liaison Program, the MIT International Science and Technology Initiatives program, or the MIT Sloan Latin America Office. 

    Datathons are typically held in that country’s national language or dialect, rather than English, with representation from academia, industry, government, and other stakeholders. Doctors, nurses, pharmacists, and social workers join up with computer science, engineering, and humanities students to brainstorm and analyze potential solutions. “They need each other’s expertise to fully leverage and discover and validate the knowledge that is encrypted in the data, and that will be translated into the way they deliver care,” says Celi. 

    “Everywhere we go, there is incredible talent that is completely capable of designing solutions to their health-care problems,” he emphasizes. The datathons aim to further empower the professionals and students in the host countries to drive medical research, innovation, and entrepreneurship.

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    Fighting built-in bias 

    Applying machine learning and other advanced data science techniques to medical data reveals that “bias exists in the data in unimaginable ways” in every type of health product, Celi says. Often this bias is rooted in the clinical trials required to approve medical devices and therapies. 

    One dramatic example comes from pulse oximeters, which provide readouts on oxygen levels in a patient’s blood. It turns out that these devices overestimate oxygen levels for people of color. “We have been under-treating individuals of color because the nurses and the doctors have been falsely assured that their patients have adequate oxygenation,” he says. “We think that we have harmed, if not killed, a lot of individuals in the past, especially during Covid, as a result of a technology that was not designed with inclusive test subjects.” 

    Such dangers only increase as the universe of medical data expands. “The data that we have available now for research is maybe two or three levels of magnitude more than what we had even 10 years ago,” Celi says. MIMIC, for example, now includes terabytes of X-ray, echocardiogram, and electrocardiogram data, all linked with related health records. Such enormous sets of data allow investigators to detect health patterns that were previously invisible. 

    “But there is a caveat,” Celi says. “It is trivial for computers to learn sensitive attributes that are not very obvious to human experts.” In a study released last year, for instance, he and his colleagues showed that algorithms can tell if a chest X-ray image belongs to a white patient or person of color, even without looking at any other clinical data. 

    “More concerningly, groups including ours have demonstrated that computers can learn easily if you’re rich or poor, just from your imaging alone,” Celi says. “We were able to train a computer to predict if you are on Medicaid, or if you have private insurance, if you feed them with chest X-rays without any abnormality. So again, computers are catching features that are not visible to the human eye.” And these features may lead algorithms to advise against therapies for people who are Black or poor, he says. 

    Opening up industry opportunities 

    Every stakeholder stands to benefit when pharmaceutical firms and other health-care corporations better understand societal needs and can target their treatments appropriately, Celi says. 

    “We need to bring to the table the vendors of electronic health records and the medical device manufacturers, as well as the pharmaceutical companies,” he explains. “They need to be more aware of the disparities in the way that they perform their research. They need to have more investigators representing underrepresented groups of people, to provide that lens to come up with better designs of health products.” 

    Corporations could benefit by sharing results from their clinical trials, and could immediately see these potential benefits by participating in datathons, Celi says. “They could really witness the magic that happens when that data is curated and analyzed by students and clinicians with different backgrounds from different countries. So we’re calling out our partners in the pharmaceutical industry to organize these events with us!”  More

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      Success at the intersection of technology and finance

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      Citadel founder and CEO Ken Griffin had some free advice for an at-capacity crowd of MIT students at the Wong Auditorium during a campus visit in April. “If you find yourself in a career where you’re not learning,” he told them, “it’s time to change jobs. In this world, if you’re not learning, you can find yourself irrelevant in the blink of an eye.”

      During a conversation with Bryan Landman ’11, senior quantitative research lead for Citadel’s Global Quantitative Strategies business, Griffin reflected on his career and offered predictions for the impact of technology on the finance sector. Citadel, which he launched in 1990, is now one of the world’s leading investment firms. Griffin also serves as non-executive chair of Citadel Securities, a market maker that is known as a key player in the modernization of markets and market structures.

      “We are excited to hear Ken share his perspective on how technology continues to shape the future of finance, including the emerging trends of quantum computing and AI,” said David Schmittlein, the John C Head III Dean and professor of marketing at MIT Sloan School of Management, who kicked off the program. The presentation was jointly sponsored by MIT Sloan, the MIT Schwarzman College of Computing, the School of Engineering, MIT Career Advising and Professional Development, and Citadel Securities Campus Recruiting.

      The future, in Griffin’s view, “is all about the application of engineering, software, and mathematics to markets. Successful entrepreneurs are those who have the tools to solve the unsolved problems of that moment in time.” He launched Citadel only one year after graduating from college. “History so far has been kind to the vision I had back in the late ’80s,” he said.

      Griffin realized very early in his career “that you could use a personal computer and quantitative finance to price traded securities in a way that was much more advanced than you saw on your typical equity trading desk on Wall Street.” Both businesses, he told the audience, are ultimately driven by research. “That’s where we formulate the ideas, and trading is how we monetize that research.”

      It’s also why Citadel and Citadel Securities employ several hundred software engineers. “We have a huge investment today in using modern technology to power our decision-making and trading,” said Griffin.

      One example of Citadel’s application of technology and science is the firm’s hiring of a meteorological team to expand the weather analytics expertise within its commodities business. While power supply is relatively easy to map and analyze, predicting demand is much more difficult. Citadel’s weather team feeds forecast data obtained from supercomputers to its traders. “Wind and solar are huge commodities,” Griffin explained, noting that the days with highest demand in the power market are cloudy, cold days with no wind. When you can forecast those days better than the market as a whole, that’s where you can identify opportunities, he added.

      Pros and cons of machine learning

      Asking about the impact of new technology on their sector, Landman noted that both Citadel and Citadel Securities are already leveraging machine learning. “In the market-making business,” Griffin said, “you see a real application for machine learning because you have so much data to parametrize the models with. But when you get into longer time horizon problems, machine learning starts to break down.”

      Griffin noted that the data obtained through machine learning is most helpful for investments with short time horizons, such as in its quantitative strategies business. “In our fundamental equities business,” he said, “machine learning is not as helpful as you would want because the underlying systems are not stationary.”

      Griffin was emphatic that “there has been a moment in time where being a really good statistician or really understanding machine-learning models was sufficient to make money. That won’t be the case for much longer.” One of the guiding principles at Citadel, he and Landman agreed, was that machine learning and other methodologies should not be used blindly. Each analyst has to cite the underlying economic theory driving their argument on investment decisions. “If you understand the problem in a different way than people who are just using the statistical models,” he said, “you have a real chance for a competitive advantage.”

      ChatGPT and a seismic shift

      Asked if ChatGPT will change history, Griffin predicted that the rise of capabilities in large language models will transform a substantial number of white collar jobs. “With open AI for most routine commercial legal documents, ChatGPT will do a better job writing a lease than a young lawyer. This is the first time we are seeing traditionally white-collar jobs at risk due to technology, and that’s a sea change.”

      Griffin urged MIT students to work with the smartest people they can find, as he did: “The magic of Citadel has been a testament to the idea that by surrounding yourself with bright, ambitious people, you can accomplish something special. I went to great lengths to hire the brightest people I could find and gave them responsibility and trust early in their careers.”

      Even more critical to success is the willingness to advocate for oneself, Griffin said, using Gerald Beeson, Citadel’s chief operating officer, as an example. Beeson, who started as an intern at the firm, “consistently sought more responsibility and had the foresight to train his own successors.” Urging students to take ownership of their careers, Griffin advised: “Make it clear that you’re willing to take on more responsibility, and think about what the roadblocks will be.”

      When microphones were handed to the audience, students inquired what changes Griffin would like to see in the hedge fund industry, how Citadel assesses the risk and reward of potential projects, and whether hedge funds should give back to the open source community. Asked about the role that Citadel — and its CEO — should play in “the wider society,” Griffin spoke enthusiastically of his belief in participatory democracy. “We need better people on both sides of the aisle,” he said. “I encourage all my colleagues to be politically active. It’s unfortunate when firms shut down political dialogue; we actually embrace it.”

      Closing on an optimistic note, Griffin urged the students in the audience to go after success, declaring, “The world is always awash in challenge and its shortcomings, but no matter what anybody says, you live at the greatest moment in the history of the planet. Make the most of it.” More

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      Learner in Afghanistan reaches beyond barriers to pursue career in data science

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      Tahmina S. was a junior studying computer engineering at a top university in Afghanistan when a new government policy banned women from pursuing education. In August 2021, the Taliban prohibited girls from attending school beyond the sixth grade. While women were initially allowed to continue to attend universities, by October 2021, an order from the Ministry of Higher Education declared that all women in Afghanistan were suspended from attending public and private centers of higher education.

      Determined to continue her studies and pursue her ambitions, Tahmina found the MIT Refugee Action Hub (ReACT) and was accepted to its Certificate in Computer Science and Data Science program in 2022.

      “ReACT helped me realize that I can do big things and be a part of big things,” she says.

      MIT ReACT provides education and professional opportunities to learners from refugee and forcibly displaced communities worldwide. ReACT’s core pillars include academic development, human skills development, employment pathways, and network building. Since 2017, ReACT has offered its Certificate in Computer and Data Science (CDS) program free-of-cost to learners wherever they live. In 2022, ReACT welcomed its largest and most diverse cohort to date — 136 learners from 29 countries — including 25 learners from Afghanistan, more than half of whom are women.

      Tahmina was able to select her classes in the program, and especially valued learning Python — which has led to her studying other programming languages and gaining more skills in data science. She’s continuing to take online courses in hopes of completing her undergraduate degree, and someday pursuing a masters degree in computer science and becoming a data scientist.

      “It’s an important and fun career. I really love data,” she says. “If this is my only time for this experience, I will bring to the table what I have, and do my best.”

      In addition to the education ban, Tahmina also faced the challenge of accessing an internet connection, which is expensive where she lives. But she regularly studies between 12 and 14 hours a day to achieve her dreams.

      The ReACT program offers a blend of asynchronous and synchronous learning. Learners complete a curated series of online, rigorous MIT coursework through MITx with the support of teaching assistants and collaborators, and also participate in a series of interactive online workshops in interpersonal skills that are critical to success in education and careers.

      ReACT learners engage with MIT’s global network of experts including MIT staff, faculty, and alumni — as well as collaborators across technology, humanitarian, and government sectors.

      “I loved that experience a lot, it was a huge achievement. I’m grateful ReACT gave me a chance to be a part of that team of amazing people. I’m amazed I completed that program, because it was really challenging.”

      Theory into practice

      Tahmina was one of 10 students from the ReACT cohort accepted to the highly competitive MIT Innovation Leadership Bootcamp program. She worked on a team of five people who initiated a business proposal and took the project through each phase of the development process. Her team’s project was creating an app for finance management for users aged 23-51 — including all the graphic elements and a final presentation. One valuable aspect of the boot camp, Tahmina says, was presenting their project to real investors who then provided business insights and actionable feedback.

      As part of this ReACT cohort, Tahmina also participated in the Global Apprenticeship Program (GAP) pilot, an initiative led by Talanta and with the participation of MIT Open Learning as curriculum provider. The GAP initiative focuses on improving diverse emerging talent job preparedness and exploring how companies can successfully recruit, onboard, and retain this talent through remote, paid internships. Through the GAP pilot, Tahmina received training in professional skills, resume and interview preparation, and was matched with a financial sector firm for a four-month remote internship in data science.

      To prepare Tahmina and other learners for these professional experiences, ReACT trains its cohorts to work with people who have diverse backgrounds, experiences, and challenges. The nonprofit Na’amal offered workshops covering areas such as problem-solving, innovation and ideation, goal-setting, communication, teamwork, and infrastructure and info security. Tahmina was able to access English classes and learn valuable career skills, such as writing a resume.“This was an amazing part for me. There’s a huge difference going from theoretical to practical,” she says. “Not only do you have to have the theoretical experience, you have to have soft skills. You have to communicate everything you learn to other people, because other people in the business might not have that knowledge, so you have to tell the story in a way that they can understand.”

      ReACT wanted the women in the program to be mentored by women who were not only leaders in the tech field, but working in the same geographic region as learners. At the start of the internship, Na’amal connected Tahmina with a mentor, Maha Gad, who is head of talent development at Talabat and lives in Dubai. Tahmina met with Gad at the beginning and end of each month, giving her the opportunity to ask expansive questions. Tahmina says Gad encouraged her to research and plan first, and then worked with her to explore new tools, like Trello.

      Wanting to put her skills to use locally, Tahmina volunteered at the nonprofit Rumie, a community for Afghan women and girls, working as a learning designer, translator, team leader, and social media manager. She currently volunteers at Correspondents of the World as a story ambassador, helping Afghan people share stories, community, and culture — especially telling the stories of Afghan women and the changes they’ve made in the world.

      “It’s been the most beautiful journey of my life that I will never forget,” says Tahmina. “I found ReACT at a time when I had nothing, and I found the most valuable thing.” More

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      Boosting passenger experience and increasing connectivity at the Hong Kong International Airport

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      Recently, a cohort of 36 students from MIT and universities across Hong Kong came together for the MIT Entrepreneurship and Maker Skills Integrator (MEMSI), an intense two-week startup boot camp hosted at the MIT Hong Kong Innovation Node.

      “We’re very excited to be in Hong Kong,” said Professor Charles Sodini, LeBel Professor of Electrical Engineering and faculty director of the Node. “The dream always was to bring MIT and Hong Kong students together.”

      Students collaborated on six teams to meet real-world industry challenges through action learning, defining a problem, designing a solution, and crafting a business plan. The experience culminated in the MEMSI Showcase, where each team presented its process and unique solution to a panel of judges. “The MEMSI program is a great demonstration of important international educational goals for MIT,” says Professor Richard Lester, associate provost for international activities and chair of the Node Steering Committee at MIT. “It creates opportunities for our students to solve problems in a particular and distinctive cultural context, and to learn how innovations can cross international boundaries.” 

      Meeting an urgent challenge in the travel and tourism industry

      The Hong Kong Airport Authority (AAHK) served as the program’s industry partner for the third consecutive year, challenging students to conceive innovative ideas to make passenger travel more personalized from end-to-end while increasing connectivity. As the travel industry resuscitates profitability and welcomes crowds back amidst ongoing delays and labor shortages, the need for a more passenger-centric travel ecosystem is urgent.

      The airport is the third-busiest international passenger airport and the world’s busiest cargo transit. Students experienced an insider’s tour of the Hong Kong International Airport to gain on-the-ground orientation. They observed firsthand the complex logistics, possibilities, and constraints of operating with a team of 78,000 employees who serve 71.5 million passengers with unique needs and itineraries.

      Throughout the program, the cohort was coached and supported by MEMSI alumni, travel industry mentors, and MIT faculty such as Richard de Neufville, professor of engineering systems.

      The mood inside the open-plan MIT Hong Kong Innovation Node was nonstop energetic excitement for the entire program. Each of the six teams was composed of students from MIT and from Hong Kong universities. They learned to work together under time pressure, develop solutions, receive feedback from industry mentors, and iterate around the clock.

      “MEMSI was an enriching and amazing opportunity to learn about entrepreneurship while collaborating with a diverse team to solve a complex problem,” says Maria Li, a junior majoring in computer science, economics, and data science at MIT. “It was incredible to see the ideas we initially came up with as a team turn into a single, thought-out solution by the end.”

      Unsurprisingly given MIT’s focus on piloting the latest technology and the tech-savvy culture of Hong Kong as a global center, many team projects focused on virtual reality, apps, and wearable technology designed to make passengers’ journeys more individualized, efficient, or enjoyable.

      After observing geospatial patterns charting passengers’ movement through an airport, one team realized that many people on long trips aim to meet fitness goals by consciously getting their daily steps power walking the expansive terminals. The team’s prototype, FitAir, is a smart, biometric token integrated virtual coach, which plans walking routes within the airport to promote passenger health and wellness.

      Another team noted a common frustration among frequent travelers who manage multiple mileage rewards program profiles, passwords, and status reports. They proposed AirPoint, a digital wallet that consolidates different rewards programs and presents passengers with all their airport redemption opportunities in one place.

      “Today, there is no loser,” said Vivian Cheung, chief operating officer of AAHK, who served as one of the judges. “Everyone is a winner. I am a winner, too. I have learned a lot from the showcase. Some of the ideas, I believe, can really become a business.”

      Cheung noted that in just 12 days, all teams observed and solved her organization’s pain points and successfully designed solutions to address them.

      More than a competition

      Although many of the models pitched are inventive enough to potentially shape the future of travel, the main focus of MEMSI isn’t to act as yet another startup challenge and incubator.

      “What we’re really focusing on is giving students the ability to learn entrepreneurial thinking,” explains Marina Chan, senior director and head of education at the Node. “It’s the dynamic experience in a highly connected environment that makes being in Hong Kong truly unique. When students can adapt and apply theory to an international context, it builds deeper cultural competency.”

      From an aerial view, the boot camp produced many entrepreneurs in the making and lasting friendships, and respect for other cultural backgrounds and operating environments.

      “I learned the overarching process of how to make a startup pitch, all the way from idea generation, market research, and making business models, to the pitch itself and the presentation,” says Arun Wongprommoon, a senior double majoring in computer science and engineering and linguistics.  “It was all a black box to me before I came into the program.”

      He said he gained tremendous respect for the startup world and the pure hard work and collaboration required to get ahead.

      Spearheaded by the Node, MEMSI is a collaboration among the MIT Innovation Initiative, the Martin Trust Center for Entrepreneurship, the MIT International Science and Technology Initiatives, and Project Manus. Learn more about applying to MEMSI. More

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      Helping companies deploy AI models more responsibly

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      Companies today are incorporating artificial intelligence into every corner of their business. The trend is expected to continue until machine-learning models are incorporated into most of the products and services we interact with every day.

      As those models become a bigger part of our lives, ensuring their integrity becomes more important. That’s the mission of Verta, a startup that spun out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Verta’s platform helps companies deploy, monitor, and manage machine-learning models safely and at scale. Data scientists and engineers can use Verta’s tools to track different versions of models, audit them for bias, test them before deployment, and monitor their performance in the real world.

      “Everything we do is to enable more products to be built with AI, and to do that safely,” Verta founder and CEO Manasi Vartak SM ’14, PhD ’18 says. “We’re already seeing with ChatGPT how AI can be used to generate data, artefacts — you name it — that look correct but aren’t correct. There needs to be more governance and control in how AI is being used, particularly for enterprises providing AI solutions.”

      Verta is currently working with large companies in health care, finance, and insurance to help them understand and audit their models’ recommendations and predictions. It’s also working with a number of high-growth tech companies looking to speed up deployment of new, AI-enabled solutions while ensuring those solutions are used appropriately.

      Vartak says the company has been able to decrease the time it takes customers to deploy AI models by orders of magnitude while ensuring those models are explainable and fair — an especially important factor for companies in highly regulated industries.

      Health care companies, for example, can use Verta to improve AI-powered patient monitoring and treatment recommendations. Such systems need to be thoroughly vetted for errors and biases before they’re used on patients.

      “Whether it’s bias or fairness or explainability, it goes back to our philosophy on model governance and management,” Vartak says. “We think of it like a preflight checklist: Before an airplane takes off, there’s a set of checks you need to do before you get your airplane off the ground. It’s similar with AI models. You need to make sure you’ve done your bias checks, you need to make sure there’s some level of explainability, you need to make sure your model is reproducible. We help with all of that.”

      From project to product

      Before coming to MIT, Vartak worked as a data scientist for a social media company. In one project, after spending weeks tuning machine-learning models that curated content to show in people’s feeds, she learned an ex-employee had already done the same thing. Unfortunately, there was no record of what they did or how it affected the models.

      For her PhD at MIT, Vartak decided to build tools to help data scientists develop, test, and iterate on machine-learning models. Working in CSAIL’s Database Group, Vartak recruited a team of graduate students and participants in MIT’s Undergraduate Research Opportunities Program (UROP).

      “Verta would not exist without my work at MIT and MIT’s ecosystem,” Vartak says. “MIT brings together people on the cutting edge of tech and helps us build the next generation of tools.”

      The team worked with data scientists in the CSAIL Alliances program to decide what features to build and iterated based on feedback from those early adopters. Vartak says the resulting project, named ModelDB, was the first open-source model management system.

      Vartak also took several business classes at the MIT Sloan School of Management during her PhD and worked with classmates on startups that recommended clothing and tracked health, spending countless hours in the Martin Trust Center for MIT Entrepreneurship and participating in the center’s delta v summer accelerator.

      “What MIT lets you do is take risks and fail in a safe environment,” Vartak says. “MIT afforded me those forays into entrepreneurship and showed me how to go about building products and finding first customers, so by the time Verta came around I had done it on a smaller scale.”

      ModelDB helped data scientists train and track models, but Vartak quickly saw the stakes were higher once models were deployed at scale. At that point, trying to improve (or accidentally breaking) models can have major implications for companies and society. That insight led Vartak to begin building Verta.

      “At Verta, we help manage models, help run models, and make sure they’re working as expected, which we call model monitoring,” Vartak explains. “All of those pieces have their roots back to MIT and my thesis work. Verta really evolved from my PhD project at MIT.”

      Verta’s platform helps companies deploy models more quickly, ensure they continue working as intended over time, and manage the models for compliance and governance. Data scientists can use Verta to track different versions of models and understand how they were built, answering questions like how data were used and which explainability or bias checks were run. They can also vet them by running them through deployment checklists and security scans.

      “Verta’s platform takes the data science model and adds half a dozen layers to it to transform it into something you can use to power, say, an entire recommendation system on your website,” Vartak says. “That includes performance optimizations, scaling, and cycle time, which is how quickly you can take a model and turn it into a valuable product, as well as governance.”

      Supporting the AI wave

      Vartak says large companies often use thousands of different models that influence nearly every part of their operations.

      “An insurance company, for example, will use models for everything from underwriting to claims, back-office processing, marketing, and sales,” Vartak says. “So, the diversity of models is really high, there’s a large volume of them, and the level of scrutiny and compliance companies need around these models are very high. They need to know things like: Did you use the data you were supposed to use? Who were the people who vetted it? Did you run explainability checks? Did you run bias checks?”

      Vartak says companies that don’t adopt AI will be left behind. The companies that ride AI to success, meanwhile, will need well-defined processes in place to manage their ever-growing list of models.

      “In the next 10 years, every device we interact with is going to have intelligence built in, whether it’s a toaster or your email programs, and it’s going to make your life much, much easier,” Vartak says. “What’s going to enable that intelligence are better models and software, like Verta, that help you integrate AI into all of these applications very quickly.” More

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      New program to support translational research in AI, data science, and machine learning

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      The MIT School of Engineering and Pillar VC today announced the MIT-Pillar AI Collective, a one-year pilot program funded by a gift from Pillar VC that will provide seed grants for projects in artificial intelligence, machine learning, and data science with the goal of supporting translational research. The program will support graduate students and postdocs through access to funding, mentorship, and customer discovery.

      Administered by the MIT Deshpande Center for Technological Innovation, the MIT-Pillar AI Collective will center on the market discovery process, advancing projects through market research, customer discovery, and prototyping. Graduate students and postdocs will aim to emerge from the program having built minimum viable products, with support from Pillar VC and experienced industry leaders.

      “We are grateful for this support from Pillar VC and to join forces to converge the commercialization of translational research in AI, data science, and machine learning, with an emphasis on identifying and cultivating prospective entrepreneurs,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Pillar’s focus on mentorship for our graduate students and postdoctoral researchers, and centering the program within the Deshpande Center, will undoubtedly foster big ideas in AI and create an environment for prospective companies to launch and thrive.” 

      Founded by Jamie Goldstein ’89, Pillar VC is committed to growing companies and investing in personal and professional development, coaching, and community.

      “Many of the most promising companies of the future are living at MIT in the form of transformational research in the fields of data science, AI, and machine learning,” says Goldstein. “We’re honored by the chance to help unlock this potential and catalyze a new generation of founders by surrounding students and postdoctoral researchers with the resources and mentorship they need to move from the lab to industry.”

      The program will launch with the 2022-23 academic year. Grants will be open only to MIT faculty and students, with an emphasis on funding for graduate students in their final year, as well as postdocs. Applications must be submitted by MIT employees with principal investigator status. A selection committee composed of three MIT representatives will include Devavrat Shah, faculty director of the Deshpande Center, the Andrew (1956) and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society; the chair of the selection committee; and a representative from the MIT Schwarzman College of Computing. The committee will also include representation from Pillar VC. Funding will be provided for up to nine research teams.

      “The Deshpande Center will serve as the perfect home for the new collective, given its focus on moving innovative technologies from the lab to the marketplace in the form of breakthrough products and new companies,” adds Chandrakasan. 

      “The Deshpande Center has a 20-year history of guiding new technologies toward commercialization, where they can have a greater impact,” says Shah. “This new collective will help the center expand its own impact by helping more projects realize their market potential and providing more support to researchers in the fast-growing fields of AI, machine learning, and data science.” 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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      in Data Management & Statistics

      Devavrat Shah appointed faculty director of the Deshpande Center

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      Devavrat Shah, the Andrew (1956) and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, has been named faculty director of the MIT Deshpande Center for Technological Innovations. The new role took effect on Feb. 1.

      Shah replaces Tim Swager, the John D. MacArthur Professor of Chemistry, who has held the position of faculty director since 2014. Working alongside Executive Director to the Deshpande Center Leon Sandler, Swager helped the Deshpande Center build an inclusive environment where innovation and entrepreneurship could thrive. By examining new models for directing, seeding, and fostering the commercialization of inventions and technology, Swager helped students and faculty breathe life into research, propelling it out of the lab and into the world as successful ventures.

      The MIT Deshpande Center for Technological Innovations is an interdepartmental center working to empower MIT’s most talented students and faculty by helping them bring new innovative technologies from the lab to the marketplace in the form of breakthrough products and new companies. Desh Deshpande founded the center with his wife, in 2002.

      “Professor Shah’s deep entrepreneurial experience coupled with his research on large complex networks will be tremendous assets to the center,” says Deshpande. “Devavrat is an impactful educator and inspiring mentor who will play a key role in the center’s mission to foster innovation and accelerate the impact of new discoveries.”

      Shah joined the Department of Electrical Engineering and Computer Science in 2005. With research focusing on statistical inference and stochastic networks, his research contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, algorithms for social data processing including ranking, recommendations and crowdsourcing, and more recently, causal inference using observational and experimental data.  

      While Shah’s work spans a range of areas across electrical engineering, computer science, and operations research, they are all tied together with the singular focus on developing algorithmic solutions for practical, challenging problems. He’s also authored two books, one on gossip algorithms in 2006 and the other on prediction methods of nearest neighbors in 2018. 

      A highly regarded teacher, Shah has been very active in curriculum development — most notably class 6.438 (Algorithms for Inference) and class 6.401 (Introduction to Statistical Data Analysis) — and has taken a leading role in developing educational programs in the statistics and data science at MIT as part of the Statistics and Data Science Center within the Institute for Data, Systems, and Society.

      “With his experience and contributions as a researcher, educator, and innovator, I have no doubt that Devavrat will excel as the next faculty director of the Deshpande Center and help usher in the next era of innovation for MIT,” says Anantha P. Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “I am grateful to Tim for the tremendous work he has done during his eight years as faculty director of the Deshpande Center. His commitment to building an inclusive environment for innovation and entrepreneurship to thrive was particularly impressive.” 

      A practiced entrepreneur, Shah co-founded Celect, Inc. — now part of Nike — in 2013, to help retailers accurately predict demand using omnichannel data. In 2019, he helped start IkigaiLabs, where he serves as CTO, with the mission to build self-driving organizations by enabling data-driven operations with human-in-the-loop with the ease of spreadsheet.

      Among his many achievements and accolades, Shah was named a Kavli Fellow of the National Academy of Science in 2014 and was just recently announced as an Institute of Electrical and Electronics Engineers (IEEE) Fellow for 2022. He’s also received a number of awards for his papers from INFORMS Applied Probability Society, INFORMS Management Science and Operations Management, NeurIPS, ACM Sigmetrics, and IEEE Infocom. His career prizes include the Erlang Prize from INFORMS Applied Probability Society and the Rising Star Award from ACM Sigmetrics. Shah has also received multiple Test of Time paper awards from ACM Sigmetrics and is recognized as a distinguished alumnus of his alma mater, the Indian Institute of Technology Bombay.

      “The Deshpande Center thanks Tim for his years of service as faculty director,” says the center’s executive director, Leon Sandler. “Tim’s commitment to innovation played an integral role in our success, and the center’s programs have thrived under his leadership. I look forward to working with Devavrat in the continuing effort to fulfill the mission of our center.”

      As part of his new post, Shah will work closely with Sandler, who has held the executive director position at the Deshpande Center since 2006. More

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      in Data Management & Statistics

      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