Developing foundations and principled, practical algorithms
for important modern learning paradigms. These include
interactive learning, distributed 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.