I am an assistant professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. Previously, I spent a year as a postdoctoral researcher at Microsoft Research, New York City. 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 specifically interested in interactive learning and learning settings involving feedback-driven data collection. My thesis focuses on interactive approaches for discovering and exploiting low-dimensional structure in data. More recently, I have been studying complex decision making problems with limited feedback, which fall under the umbrella of reinforcement learning.
Spring 2018: CS 311: Introduction to Algorithms
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable.
PAC Reinforcement Learning from Rich Observations.
Minimax Structured Normal Means Inference.
Low-Rank Matrix and Tensor Completion Via Adaptive Sampling.
Efficient Active Algorithms for Hierarchical Clustering.