I am a principal researcher at Microsoft Research, New York City. Previously, I spent two years as an assistant professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst and a year as a postdoctoral researcher at Microsoft Research, NYC. Before that, I completed my PhD in the Computer Science Department at Carnegie Mellon University, advised by Aarti Singh. I received my undergraduate degree in EECS at UC Berkeley.
My research interests are in machine learning and statistics. I am most excited about interactive learning, or learning settings that involve feedback-driven data collection. My recent interests revolve around decision making problems with limited feedback, including contextual bandits and reinforcement learning.
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Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm. Tongyi Cao and Akshay Krishnamurthy.
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms.
Contextual decision processes with low Bellman rank are PAC-learnable.
Low-rank matrix and tensor completion via adaptive sampling.
Efficient active algorithms for hierarchical clustering.