My research group is engaged in fundamental research in the following areas:

• 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. Another focus is on dependent and/or non-stationary data (such as in time series applications)
• 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 data.
• 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 forward the frontier in sequential decision making with a focus on delivering personalized health interventions via mobile devices. Here is a short video from a talk I gave at NYAS and an article from ISR Sampler. A related seminar series that I ran during 2013-2018 is archived here.

My alma maters 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 Pradeep Ravikumar.

I am one of the program leaders for SAMSI's Program on Statistical, Mathematical, and Computational Methods for Precision Medicine.

Activities at U-M I'm involved with:

• HeartSteps: Mobile Technology for Encouraging People to Stay Physically Active
• Learning Analytics at Michigan
• Michigan Institute for Data Science (MIDAS)
• MIDAS Project: Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology
• NSF grant RTG: Understanding dynamic big data with complex structure
• Precision Health Initiative