Department of Statistics
Department of Electrical Engineering and Computer Science (by courtesy)
University of Michigan, Ann Arbor
Are we deranged?
My research group is engaged in fundamental research in the following areas:
I like to work in multi-disciplinary teams and am always interested in
discussing challenging machine learning problems in any scientific field including behavioral sciences,
chemistry, learning sciences, life sciences, and network science.
Statistical learning theory: We are developing theory and algorithms for
predictions problems (e.g., learning to rank and multilabel learning) with complex label
spaces and where the available human supervision is often weak.
Sequential prediction in a game theoretic framework: We are trying to
understand the power and limitations of sequential prediction algorithms
when no probabilistic assumptions are placed on the data generating mechanism.
High dimensional and network data analysis: We are developing scalable algorithms with
provable performance guarantees for learning from high dimensional and network
Optimization algorithms: We are creating incremental, distributed and parallel
algorithms for machine learning problems arising in today's data rich world.
Reinforcement learning: We are synthesizing and further developing concepts and techniques from
artificial intelligence, control theory and operations research for pushing
frontier in sequential decision making with a focus on delivering personalized
health interventions via mobile devices.
My almae matres are IIT Kanpur (B.Tech., 2002) and UC Berkeley
(M.A., 2005 and Ph.D., 2007. Advisor: Peter Bartlett).
I was a research assistant professor at TTIC from 2008 to 2010.
From 2010 to 2012, I was a post-doctoral fellow at UT Austin where I worked with Inderjit Dhillon and
Activities at U-M I'm involved with:
Students and Postdocs
Publications (also see my
454 West Hall
1085 South University
Ann Arbor, MI 48109-1107