I am an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley
I work broadly on the theoretical aspects of machine learning and algorithmic economics. Classically, the outcome of a learning algorithm is considered in isolation from the effects that it may have on the process that generates the data or the party who is interested in learning. In today's world, increasingly more people and organizations interact with learning systems, making it necessary to consider these effects. My work builds theoretical foundations for ensuring both the performance of learning algorithms in presence of everyday societal and economic forces and the integrity of social and economic forces that are born out of the use of machine learning systems.
Addressing machine learning in this context calls for approaches that align the incentives and interests of the learners and other parties, are robust to the evolving social and economic needs, and promote equity. My work in machine learning, economics, and theory of computer science addresses emerging fields such as learning in economic and societal settings, collaborative learning, robustness of ML, fairness and privacy.
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.