I am an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. I am a co-director of the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB) and a member of the Berkeley Artificial Intelligence Research (BAIR) Lab and the Theory Group at UC Berkeley.

I work on a broad and versatile set of problems related to machine learning, algorithms, economics, and society. My work contributes to an emerging mathematical foundation for learning and decision-making systems in the presence of economic and societal forces. Important problem settings often addressed by my work include collaborative (and federated) learning, learning in markets and other economics settings, incentive-aware and robust learning, and foundations of machine learning generally. My work has been recognized by a Sloan fellowship (2024), Schmidt Sciences AI2050 award, NSF CAREER (2022), Google Research scholar award (2023), NeurIPS and ICAPS best paper awards, EC exemplary track paper awards, and several other industry awards and fellowships.

Previously, I was an assistant professor in the CS department of Cornell University, in 2019-2020. Prior to that, I was a postdoctoral researcher at Microsoft Research, New England, in 2018-2019. I received my Ph.D. from the Computer Science Department of Carnegie Mellon University, where I was fortunate to be co-advised by Avrim Blum and Ariel Procaccia. My thesis titled Foundation of Machine Learning, by the People, for the People received the CMU School of Computer Science Dissertation Award (2018) and a SIGecom Dissertation Honorable Mention Award (2019).

My CV can be found here.

Graduate Students

• Nivasini Ananthakrishnan: Ph.D. student (co-advised with Jordan).
• Mark Bedaywi: Ph.D. student (co-advised with Russell).
• Jessica Dai: Ph.D. student (co-advised with Recht).
• Haozhe (Eric) Jiang: Ph.D. student (co-advised with Jiao).
• Brian Lee: Ph.D. student (co-advised with Recht).
• Abhishek Shetty: Ph.D. student.
• Kunhe Yang: Ph.D. student.
• Eric Zhao: Ph.D. student (co-advised with Jordan).
• Alex Wei: Ph.D. 2023 (co-advised with Jordan and Steinhardt). Now Researcher at OpenAI.

Past and Current Postdoctoral Researchers

• Mahsa Derakhshan: FODSI Postdoc, now Assistant Professor at Northeastern University.
• Chara Podimata: FODSI Postdoc, now Assistant Professor at MIT Sloan.
• Yuval Dagan: FODSI Postdoc, now senior Lecturer at Tel-Aviv University.
• Paul Gölz: FODSI-MSRI Postdoc, now Assitant Professor at Cornell ORIE.
• Mingda Qiao: FODSI Postdoc. Incoming Assistant Professor UMass Amherst.

Undergraduate Students

• Naveen Durvasula: M.E.T student (double major in EECS and BA).

Past and Current Short-term visitors

• Bailey Flanigan: Ph.D. student at Carnegie Mellon University.
• Paul Goelz: Postdoctoral researcher at Harvard University.
• Richard Phillips: Ph.D. student at Cornell University.

Tutorials

• Learning and Incentives, Simons Institute's Learning and Games Boot Camp. (Slides, Videos Part I, Part II, Part III, and Part IV)
• Multi-Distribution Learning, for Fairness, Robustness, and Collaboration, Simons Institute's Data-Driven Processes Bootcamp. (Slides and video)

Workshops

• NeurIPS-19 Workshop on Bridging Game Theory and Deep Learning, December 2019.
• EC-19 Workshop on Learning in the Presence of Strategic Behavior, June 2019.
• Toyota Technological Institute Workshop on Learning in the Presence of Strategic Behavior, August 2018
• NIPS-17 Workshop on Learning in the Presence of Strategic Behavior, December 2017.

Selected Senior-level Service and Committee work

• Program Committe co-Chair for ALT 2022.
• Steering Committe member, Association for Computational Learning (ACL).
• Director, Association for Algorthmic Learning Theory (AALT)
• Co-Founder of Learning Theory Alliance: A mentorship network with regular mentorship activities for the learning theory community. 2020 -- present .