Developing foundations and principled, practical algorithms
for important modern learning paradigms. These include
interactive learning, distributed learning, transfer learning,
multi-task learning, and life-long learning. My research
formalizes and explicitly addresses all constraints and
important challenges of these new settings, including
statistical efficiency, computational efficiency, noise
tolerance, limited supervision or interaction, privacy, low
communication, and incentives.
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
Computational aspects in game theory and economics.
Analysis of the algorithms beyond the worst case and more
generally identifying interesting and realistic models of
computation that provide a better alternative to traditional
worst-case models in a broad range of optimization problems.