Events
Colloquium
Recommendations in the NYC High School Match (and other high-stakes settings)
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Speaker: Nikhil Garg (Cornell) Assistant Professor of Operations Research and Information Engineering at Cornell Tech Cornell University Wednesday, April 8, 2026 12:00PM - 1:00PM Lunch at 11:30am in 1307
Talk 12:00-1:00pm in 1327 Location: Yale Institute for Foundations of Data Science & Webcast, 219 Prospect Street, 13th Floor, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=78b0fbd3-c731-4d49-8821-b3ca014bd219 |
Abstract: Recommendation systems are now used in high-stakes settings, including to help find jobs, schools, and partners. Building public interest recommender systems in such settings bring both individual-level (enabling exploration, diversity, data quality) and societal (fairness, capacity constraints, monoculture) challenges. I will talk about an ongoing collaboration with the NYC Public Schools, in which we designed and deployed an informational intervention to help students from underserved middle schools discover high-performing, nearby high schools where they have a strong individual admissions likelihood. However, recommending specific programs brings a methodological challenge, congestion: if many applicants are recommended the same program, affecting admissions likelihoods, then the recommendations may be self-defeating. Time permitting, I’ll also overview other directions in tackling such challenges, including on (a) algorithmic monoculture and LLM homogeneity, (b) a platform to help discharge patients to long-term care facilities, and (c) feed ranking algorithms on Bluesky for research paper recommendations.
Speaker Bio: Nikhil Garg joined the Cornell University faculty as an Assistant Professor of Operations Research and Information Engineering at Cornell Tech in July 2021.
Garg’s research is at the intersection of computer science, economics, and operations—on the application of algorithms, data science, and mechanism design to the study of democracy, markets, and societal systems at large. His research interests include surge pricing, rating systems, how to vote on budgets, the role of testing in college admissions, stereotypes in word embeddings, and polarization on Twitter.
Garg received his Ph.D. from Stanford University in 2020, where he was part of the Society and Algorithms Lab and Stanford Crowdsourced Democracy Team. He also received a B.S. and B.A. degrees from the University of Texas at Austin in 2015.
He has spent time at Uber, NASA, Microsoft, the Texas Senate, and IEEE’s policy arm, and most recently was the principal data scientist at PredictWise—which provides election analytics for political campaigns—and is currently completing a postdoc at the University of California, Berkeley in the Department of Electrical Engineering and Computer Science.
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