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    Devavrat Shah appointed faculty director of the Deshpande Center

    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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    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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    Reducing food waste to increase access to affordable foods

    About a third of the world’s food supply never gets eaten. That means the water, labor, energy, and fertilizer that went into growing, processing, and distributing the food is wasted.

    On the other end of the supply chain are cash-strapped consumers, who have been further distressed in recent years by factors like the Covid-19 pandemic and inflation.

    Spoiler Alert, a company founded by two MIT alumni, is helping companies bridge the gap between food waste and food insecurity with a platform connecting major food and beverage brands with discount grocers, retailers, and nonprofits. The platform helps brands discount or donate excess and short-dated inventory days, weeks, and months before it expires.

    “There is a tremendous amount of underutilized data that exists in the manufacturing and distribution space that results in good food going to waste,” says Ricky Ashenfelter MBA ’15, who co-founded the company with Emily Malina MBA ’15.

    Spoiler Alert helps brands manage distressed inventory data, create offers for potential buyers, and review and accept bids. The platform is designed to work with companies’ existing inventory and fulfillment systems, using automation and pricing intelligence to further streamline sales.

    “At a high level, we’re a waste-prevention software built for sales and supply-chain teams,” Ashenfelter says. “You can think of it as a private [business-to-business] eBay of sorts.”

    Spoiler Alert is working with global companies like Nestle, Kraft Heinz, and Danone, as well as discount grocers like the United Grocery Outlet and Misfits Market. Those brands are already using the platform to reduce food waste and get more food on people’s tables.

    “Project Drawdown [a nonprofit working on climate solutions] has identified food waste as the number one priority to address the global climate crisis, so these types of corporate initiatives can be really powerful from an environmental standpoint,” Ashenfelter says, noting the nonprofit estimates food waste accounts for 8 percent of global greenhouse gas emissions. “Contrast that with growing levels of food insecurity and folks not being able to access affordable nutrition, and you start to see how tackling supply-chain inefficiency can have a dramatic impact from both an environmental and a social lens. That’s what motivates us.”

    Untapped data for change

    Ashenfelter came to MIT’s Sloan School of Management after several years in sustainability software and management consulting within the retail and consumer products industries.

    “I was really attracted to transitioning into something much more entrepreneurial, and to leverage not only Sloan’s focus on entrepreneurship, but also the broader MIT ecosystem’s focus on technology, entrepreneurship, clean tech innovation, and other themes along that front,” he says.

    Ashenfelter met Malina at one of Sloan’s admitted students events in 2013, and the founders soon set out to use data to decrease food waste.

    “For us, the idea was clear: How do we better leverage data to manage excess and short-dated inventory?” Ashenfelter says. “How we go about that has evolved over the last six years, but it’s all rooted in solving an enormous climate problem, solving a major food insecurity problem, and from a capitalistic standpoint, helping businesses cut costs and generate revenue from otherwise wasted products.”

    The founders spent many hours in the Martin Trust Center for MIT Entrepreneurship with support from the Sloan Sustainability Initiative, and used Spoiler Alert as a case study in nearly every class they took, thinking through product development, sales, marketing, pricing, and more through their coursework.

    “We brought our idea into just about every action learning class that we could at Sloan and MIT,” Ashenfelter says.

    They also participated in the MIT $100K Entrepreneurship Competition and received support from the Venture Mentoring Service and the IDEAS Global Challenge program.

    Upon graduation, the founders initially began building a platform to facilitate donations of excess inventory, but soon learned big companies’ processes for discounting that inventory were also highly manual. Today, more than 90 percent of Spoiler Alert’s transaction volume is discounted, with the remainder donated.

    Different teams within an organization can upload excess inventory reports to Spoiler Alert’s system, eliminating the need to manually aggregate datasets and preparing what the industry refers to as “blowout lists” to sell. Spoiler Alert uses machine-learning-based tools to help both parties with pricing and negotiations to close deals more quickly.

    “Companies are taking pretty manual and slow approaches to deciding [what to do with excess inventory],” Ashenfelter says. “And when you have slow decision-making, you’re losing days or even weeks of shelf life on that product. That can be the difference between selling product versus donating, and donating versus dumping.”

    Once a deal has been made, Spoiler Alert automatically generates the forms and workflows needed by fulfillment teams to get the product out the door. The relationships companies build on the platform are also a major driver for cutting down waste.

    “We’re providing suppliers with the ability to control where their discounted and donated product ends up,” Ashenfelter says. “That’s really powerful because it allows these CPG brands to ensure that this product is, in many cases, getting to affordable nutrition outlets in underserved communities.”

    Ashenfelter says the majority of inventory goes to regional and national discount grocers, supplemented with extensive purchasing from local and nonprofit grocery chains.

    “Everything we do is oriented around helping sell as much product as possible to a reputable set of buyers at the most fair, equitable prices possible,” Ashenfelter says.

    Scaling for impact

    The pandemic has disrupted many aspects of the food supply chains. But Ashenfelter says it has also accelerated the adoption of digital solutions that can better manage such volatility.

    When Campbell began using Spoiler Alert’s system in 2019, for instance, it achieved a 36 percent increase in discount sales and a 27 percent increase in donations over the first five months.

    Ashenfelter says the results have proven that companies’ sustainability targets can go hand in hand with initiatives that boost their bottom lines. In fact, because Spoiler Alert focuses so much on the untapped revenue associated with food waste, many customers don’t even realize Spoiler Alert is a sustainability company until after they’ve signed on.

    “What’s neat about this program is that it becomes an incredibly powerful case study internally for how sustainability and operational outcomes aren’t in conflict and can drive both business results as well as overall environmental impact,” Ashenfelter says.

    Going forward, Spoiler Alert will continue building out algorithmic solutions that could further cut down on waste internationally and across a wider array of products.

    “At every step in our process, we’re collecting a tremendous amount of data in terms of what is and isn’t selling, at what price point, to which buyers, out of which geographies, and with how much remaining shelf life,” Ashenfelter explains. “We are only starting to scratch the surface in terms of bringing our recommendations engine to life for our suppliers and buyers. Ultimately our goal is to power the waste-free economy, and rooted in that is making better decisions faster, in collaboration with a growing ecosystem of supply chain partners, and with as little manual intervention as possible.” More

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    Enabling AI-driven health advances without sacrificing patient privacy

    There’s a lot of excitement at the intersection of artificial intelligence and health care. AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more.

    By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients — AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it’s difficult to verify that researchers are only using the data they need and deleting it after they’re done.

    Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner’s system. Health care organizations can control how their datasets are used, while researchers can protect the confidentiality of their models and search queries. Neither party needs to see the data or the model to collaborate.

    SAIL’s platform can also combine data from multiple sources, creating rich insights that fuel more effective algorithms.

    “You shouldn’t have to schmooze with hospital executives for five years before you can run your machine learning algorithm,” says SAIL co-founder and MIT Professor Manolis Kellis, who co-founded the company with CEO Anne Kim ’16, SM ’17. “Our goal is to help patients, to help machine learning scientists, and to create new therapeutics. We want new algorithms — the best algorithms — to be applied to the biggest possible data set.”

    SAIL has already partnered with hospitals and life science companies to unlock anonymized data for researchers. In the next year, the company hopes to be working with about half of the top 50 academic medical centers in the country.

    Unleashing AI’s full potential

    As an undergraduate at MIT studying computer science and molecular biology, Kim worked with researchers in the Computer Science and Artificial Intelligence Laboratory (CSAIL) to analyze data from clinical trials, gene association studies, hospital intensive care units, and more.

    “I realized there is something severely broken in data sharing, whether it was hospitals using hard drives, ancient file transfer protocol, or even sending stuff in the mail,” Kim says. “It was all just not well-tracked.”

    Kellis, who is also a member of the Broad Institute of MIT and Harvard, has spent years establishing partnerships with hospitals and consortia across a range of diseases including cancers, heart disease, schizophrenia, and obesity. He knew that smaller research teams would struggle to get access to the same data his lab was working with.

    In 2017, Kellis and Kim decided to commercialize technology they were developing to allow AI algorithms to run on encrypted data.

    In the summer of 2018, Kim participated in the delta v startup accelerator run by the Martin Trust Center for MIT Entrepreneurship. The founders also received support from the Sandbox Innovation Fund and the Venture Mentoring Service, and made various early connections through their MIT network.

    To participate in SAIL’s program, hospitals and other health care organizations make parts of their data available to researchers by setting up a node behind their firewall. SAIL then sends encrypted algorithms to the servers where the datasets reside in a process called federated learning. The algorithms crunch the data locally in each server and transmit the results back to a central model, which updates itself. No one — not the researchers, the data owners, or even SAIL —has access to the models or the datasets.

    The approach allows a much broader set of researchers to apply their models to large datasets. To further engage the research community, Kellis’ lab at MIT has begun holding competitions in which it gives access to datasets in areas like protein function and gene expression, and challenges researchers to predict results.

    “We invite machine learning researchers to come and train on last year’s data and predict this year’s data,” says Kellis. “If we see there’s a new type of algorithm that is performing best in these community-level assessments, people can adopt it locally at many different institutions and level the playing field. So, the only thing that matters is the quality of your algorithm rather than the power of your connections.”

    By enabling a large number of datasets to be anonymized into aggregate insights, SAIL’s technology also allows researchers to study rare diseases, in which small pools of relevant patient data are often spread out among many institutions. That has historically made the data difficult to apply AI models to.

    “We’re hoping that all of these datasets will eventually be open,” Kellis says. “We can cut across all the silos and enable a new era where every patient with every rare disorder across the entire world can come together in a single keystroke to analyze data.”

    Enabling the medicine of the future

    To work with large amounts of data around specific diseases, SAIL has increasingly sought to partner with patient associations and consortia of health care groups, including an international health care consulting company and the Kidney Cancer Association. The partnerships also align SAIL with patients, the group they’re most trying to help.

    Overall, the founders are happy to see SAIL solving problems they faced in their labs for researchers around the world.

    “The right place to solve this is not an academic project. The right place to solve this is in industry, where we can provide a platform not just for my lab but for any researcher,” Kellis says. “It’s about creating an ecosystem of academia, researchers, pharma, biotech, and hospital partners. I think it’s the blending all of these different areas that will make that vision of medicine of the future become a reality.” More

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    End-to-end supply chain transparency

    For years, companies have managed their extended supply chains with intermittent audits and certifications while attempting to persuade their suppliers to adhere to certain standards and codes of conduct. But they’ve lacked the concrete data necessary to prove their supply chains were working as they should. They most likely had baseline data about their suppliers — what they bought and who they bought it from — but knew little else about the rest of the supply chain.

    With Sourcemap, companies can now trace their supply chains from raw material to finished good with certainty, keeping track of the mines and farms that produce the commodities they rely on to take their goods to market. This unprecedented level of transparency provides Sourcemap’s customers with the assurance that the entire end-to-end supply chain operates within their standards while living up to social and environmental targets.

    And they’re doing it at scale for large multinationals across the food, agricultural, automotive, tech, and apparel industries. Thanks to Sourcemap founder and CEO Leonardo Bonanni MA ’03, SM ’05, PhD ’10, companies like VF Corporation, owner of brands like Timberland, The North Face, Mars, Hershey, and Ferrero, now have enough data to confidently tell the story of how they’re sourcing their raw materials.

    “Coming from the Media Lab, we recognized early on the power of the cloud, the power of social networking-type databases and smartphone diffusion around the world,” says Bonanni of his company’s MIT roots. Rather than providing intermittent glances at the supply chain via an auditor, Sourcemap collects data continuously, in real-time, every step of the way, flagging anything that could indicate counterfeiting, adulteration, fraud, waste, or abuse.

    “We’ve taken our customers from a situation where they had very little control to a world where they have direct visibility over their entire global operations, even allowing them to see ahead of time — before a container reaches the port — whether there is any indication that there might be something wrong with it,” says Bonanni.

    The key problem Sourcemap addresses is a lack of data in companies’ supply chain management databases. According to Bonanni, most Sourcemap customers have invested millions of dollars in enterprise resource planning (ERP) databases, which provide information about internal operations and direct suppliers, but fall short when it comes to global operations, where their secondary and tertiary suppliers operate. Built on relational databases, ERP systems have been around for more than 40 years and work well for simple, static data structures. But they aren’t agile enough to handle big data and rapidly evolving, complex data structures

    Sourcemap, on the other hand, uses NoSQL (non-relational) database technology, which is more flexible, cost-efficient, and scalable. “Our platform is like a LinkedIn for the supply chain,” explains Bonanni. Customers provide information about where they buy their raw materials, the suppliers get invited to the network and provide information to validate those relationships, right down to the farms and the mines where the raw materials are extracted — which is often where the biggest risks lie.

    Initially, the entire supply chain database of a Sourcemap customer might amount to a few megabytes of spreadsheets listing their purchase orders and the names of their suppliers. Sourcemap delivers terabytes of data that paint a detailed picture of the supply chain, capturing everything, right down to the moment a farmer in West Africa delivers cocoa beans to a warehouse, onto a truck heading to a port, to a factory, all the way to the finished goods.

    “We’ve seen the amount of data collected grow by a factor of 1 million, which tells us that the world is finally ready for full visibility of supply chains,” says Bonanni. “The fact is that we’ve seen supply chain transparency go from a fringe concern to a broad-based requirement as a license to operate in most of Europe and North America,” says Bonanni.

    These days, disruptions in supply chains, combined with price volatility and new laws requiring companies to prove that the goods they import were not made illegally (such as by causing deforestation or involving forced or child labor), means that companies are often required to know where they source their raw materials from, even if they only import the materials through an intermediary.

    Sourcemap uses its full suite of tools to walk customers through a step-by-step process that maps their suppliers while measuring performance, ultimately verifying the entire supply chain and providing them with the confidence to import goods while being customs-compliant. At the end of the day, Sourcemap customers can communicate to their stakeholders and the end consumer exactly where their commodities come from while ensuring that social, environmental, and compliance standards are met.

    The company was recently named to the newest cohort of firms honored by the MIT Startup Exchange (STEX) as STEX25 startups. Bonanni is quick to point out the benefits of STEX and of MIT’s Industrial Liaison Program (ILP): “Our best feedback and our most constructive relationships have been with companies that sponsored our research early on at the Media Lab and ILP,” he says. “The innovative exchange of ideas inherent in the MIT startup ecosystem has helped to build up Sourcemap as a company and to grow supply chain transparency as a future-facing technology that more and more companies are now scrambling to adopt.” More

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    Helping companies optimize their websites and mobile apps

    Creating a good customer experience increasingly means creating a good digital experience. But metrics like pageviews and clicks offer limited insight into how much customers actually like a digital product.

    That’s the problem the digital optimization company Amplitude is solving. Amplitude gives companies a clearer picture into how users interact with their digital products to help them understand exactly which features to promote or improve.

    “It’s all about using product data to drive your business,” says Amplitude CEO Spenser Skates ’10, who co-founded the company with Curtis Liu ’10 and Stanford University graduate Jeffrey Wang. “Mobile apps and websites are really complex. The average app or website will have thousands of things you can do with it. The question is how you know which of those things are driving a great user experience and which parts are really frustrating for users.”

    Amplitude’s database can gather millions of details about how users behave inside an app or website and allow customers to explore that information without needing data science degrees.

    “It provides an interface for very easy, accessible ways of looking at your data, understanding your data, and asking questions of that data,” Skates says.

    Amplitude, which recently announced it will be going public, is already helping 23 of the 100 largest companies in the U.S. Customers include media companies like NBC, tech companies like Twitter, and retail companies like Walmart.

    “Our platform helps businesses understand how people are using their apps and websites so they can create better versions of their products,” Skates says. “It’s all about creating a really compelling product.”

    Learning entrepreneurship

    The founders say their years at MIT were among the best of their lives. Skates and Liu were undergraduates from 2006 to 2010. Skates majored in biological engineering while Liu majored in mathematics and electrical engineering and computer science. The two first met as opponents in MIT’s Battlecode competition, in which students use artificial intelligence algorithms to control teams of robots that compete in a strategy game against other teams. The following year they teamed up.

    “There are a lot of parallels between what you’re trying to do in Battlecode and what you end up having to do in the early stages of a startup,” Liu says. “You have limited resources, limited time, and you’re trying to accomplish a goal. What we found is trying a lot of different things, putting our ideas out there and testing them with real data, really helped us focus on the things that actually mattered. That method of iteration and continual improvement set the foundation for how we approach building products and startups.”

    Liu and Skates next participated in the MIT $100K Entrepreneurship Competition with an idea for a cloud-based music streaming service. After graduation, Skates began working in finance and Liu got a job at Google, but they continued pursuing startup ideas on the side, including a website that let alumni see where their classmates ended up and a marketplace for finding photographers.

    A year after graduation, the founders decided to quit their jobs and work on a startup full time. Skates moved into Liu’s apartment in San Francisco, setting up a mattress on the floor, and they began working on a project that became Sonalight, a voice recognition app. As part of the project, the founders built an internal system to understand where users got stuck in the app and what features were used the most.

    Despite getting over 100,000 downloads, the founders decided Sonalight was a little too early for its time and started thinking their analytics feature could be useful to other companies. They spoke with about 30 different product teams to learn more about what companies wanted from their digital analytics. Amplitude was officially founded in 2012.

    Amplitude gathers fine details about digital product usage, parsing out individual features and actions to give customers a better view of how their products are being used. Using the data in Amplitude’s intuitive, no-code interface, customers can make strategic decisions like whether to launch a feature or change a distribution channel.

    The platform is designed to ease the bottlenecks that arise when executives, product teams, salespeople, and marketers want to answer questions about customer experience or behavior but need the data science team to crunch the numbers for them.

    “It’s a very collaborative interface to encourage customers to work together to understand how users are engaging with their apps,” Skates says.

    Amplitude’s database also uses machine learning to segment users, predict user outcomes, and uncover novel correlations. Earlier this year, the company unveiled a service called Recommend that helps companies create personalized user experiences across their entire platform in minutes. The service goes beyond demographics to personalize customer experiences based on what users have done or seen before within the product.

    “We’re very conscious on the privacy front,” Skates says. “A lot of analytics companies will resell your data to third parties or use it for advertising purposes. We don’t do any of that. We’re only here to provide product insights to our customers. We’re not using data to track you across the web. Everyone expects Netflix to use the data on what you’ve watched before to recommend what to watch next. That’s effectively what we’re helping other companies do.”

    Optimizing digital experiences

    The meditation app Calm is on a mission to help users build habits that improve their mental wellness. Using Amplitude, the company learned that users most often use the app to get better sleep and reduce stress. The insights helped Calm’s team double down on content geared toward those goals, launching “sleep stories” to help users unwind at the end of each day and adding content around anxiety relief and relaxation. Sleep stories are now Calm’s most popular type of content, and Calm has grown rapidly to millions of people around the world.

    Calm’s story shows the power of letting user behavior drive product decisions. Amplitude has also helped the online fundraising site GoFundMe increase donations by showing users more compelling campaigns and the exercise bike company Peloton realize the importance of social features like leaderboards.

    Moving forward, the founders believe Amplitude’s platform will continue helping companies adapt to an increasingly digital world in which users expect more compelling, personalized experiences.

    “If you think about the online experience for companies today compared to 10 years ago, now [digital] is the main point of contact, whether you’re a media company streaming content, a retail company, or a finance company,” Skates says. “That’s only going to continue. That’s where we’re trying to help.” 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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    A comprehensive study of technological change

    The societal impacts of technological change can be seen in many domains, from messenger RNA vaccines and automation to drones and climate change. The pace of that technological change can affect its impact, and how quickly a technology improves in performance can be an indicator of its future importance. For decision-makers like investors, entrepreneurs, and policymakers, predicting which technologies are fast improving (and which are overhyped) can mean the difference between success and failure.

    New research from MIT aims to assist in the prediction of technology performance improvement using U.S. patents as a dataset. The study describes 97 percent of the U.S. patent system as a set of 1,757 discrete technology domains, and quantitatively assesses each domain for its improvement potential.

    “The rate of improvement can only be empirically estimated when substantial performance measurements are made over long time periods,” says Anuraag Singh SM ’20, lead author of the paper. “In some large technological fields, including software and clinical medicine, such measures have rarely, if ever, been made.”

    A previous MIT study provided empirical measures for 30 technological domains, but the patent sets identified for those technologies cover less than 15 percent of the patents in the U.S. patent system. The major purpose of this new study is to provide predictions of the performance improvement rates for the thousands of domains not accessed by empirical measurement. To accomplish this, the researchers developed a method using a new probability-based algorithm, machine learning, natural language processing, and patent network analytics.

    Overlap and centrality

    A technology domain, as the researchers define it, consists of sets of artifacts fulfilling a specific function using a specific branch of scientific knowledge. To find the patents that best represent a domain, the team built on previous research conducted by co-author Chris Magee, a professor of the practice of engineering systems within the Institute for Data, Systems, and Society (IDSS). Magee and his colleagues found that by looking for patent overlap between the U.S. and international patent-classification systems, they could quickly identify patents that best represent a technology. The researchers ultimately created a correspondence of all patents within the U.S. patent system to a set of 1,757 technology domains.

    To estimate performance improvement, Singh employed a method refined by co-authors Magee and Giorgio Triulzi, a researcher with the Sociotechnical Systems Research Center (SSRC) within IDSS and an assistant professor at Universidad de los Andes in Colombia. Their method is based on the average “centrality” of patents in the patent citation network. Centrality refers to multiple criteria for determining the ranking or importance of nodes within a network.

    “Our method provides predictions of performance improvement rates for nearly all definable technologies for the first time,” says Singh.

    Those rates vary — from a low of 2 percent per year for the “Mechanical skin treatment — Hair removal and wrinkles” domain to a high of 216 percent per year for the “Dynamic information exchange and support systems integrating multiple channels” domain. The researchers found that most technologies improve slowly; more than 80 percent of technologies improve at less than 25 percent per year. Notably, the number of patents in a technological area was not a strong indicator of a higher improvement rate.

    “Fast-improving domains are concentrated in a few technological areas,” says Magee. “The domains that show improvement rates greater than the predicted rate for integrated chips — 42 percent, from Moore’s law — are predominantly based upon software and algorithms.”

    TechNext Inc.

    The researchers built an online interactive system where domains corresponding to technology-related keywords can be found along with their improvement rates. Users can input a keyword describing a technology and the system returns a prediction of improvement for the technological domain, an automated measure of the quality of the match between the keyword and the domain, and patent sets so that the reader can judge the semantic quality of the match.

    Moving forward, the researchers have founded a new MIT spinoff called TechNext Inc. to further refine this technology and use it to help leaders make better decisions, from budgets to investment priorities to technology policy. Like any inventors, Magee and his colleagues want to protect their intellectual property rights. To that end, they have applied for a patent for their novel system and its unique methodology.

    “Technologies that improve faster win the market,” says Singh. “Our search system enables technology managers, investors, policymakers, and entrepreneurs to quickly look up predictions of improvement rates for specific technologies.”

    Adds Magee: “Our goal is to bring greater accuracy, precision, and repeatability to the as-yet fuzzy art of technology forecasting.” More