My main research interests are in machine learning, artificial intelligence, and theoretical computer science. Current research focus includes:

Developing foundations and principled, practical algorithms for important modern learning paradigms. These include interactive learning, distributed learning, learning representations, life-long learning, and metalearning. My research addresses important challenges of these settings, including statistical efficiency, computational efficiency, noise tolerance, limited supervision or interaction, privacy, low communication, and incentives.

Foundations and applications of data driven algorithm design. Design and analysis of algorithms on realistic instances (a.k.a. beyond worst case).

Computational and data-driven approaches in game theory and economics.

Computational, learning theoretic, and game theoretic aspects of multi-agent systems. Analyzing the overall behavior of complex systems in which multiple agents with limited information are adapting their behavior based on past experience, both in social and engineered systems contexts.