Abstract: Recommendation and search 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, algorithmic monoculture) challenges. In this talk, I'll discuss our theoretical, empirical, and deployment work in tackling these challenges, including ongoing work on (a) applicant behavior and recommendations for the NYC HS match, (b) a platform to help discharge patients to long-term care facilities, (c) feed ranking algorithms on Bluesky for research paper recommendations, including the design of steerable and interpretable recommender systems.

Bio: Nikhil Garg is an Assistant Professor of Operations Research and Information Engineering at Cornell Tech as part of the Jacobs Institute. He uses algorithms, data science, and mechanism design approaches to study democracy, markets, and societal systems at large. Nikhil has received the NSF CAREER, INFORMS George Dantzig Dissertation Award, an honorable mention for the ACM SIGecom dissertation award, several other best paper awards, and Forbes 30 under 30 for Science. He received his PhD from Stanford University and has spent considerable collaborating with government agencies and non-profits.