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. Sequential prediction in a game theoretic framework: We are trying to understand the power and limitations of sequential predictions 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 concepts and techniques from artificial intelligence, control theory and operations research for pushing the frontier in sequential decision making with a focus on delivering personalized health interventions via mobile devices. My research group is pursuing and continues to actively search for challenging machine learning problems that arise across disciplines including behavioral sciences, computational biology, computational chemistry, learning sciences, and network science.
I develop clinical trial designs and data analytic methods for informing sequential decision making in health. In particular I focus on methods for constructing real time individualized sequences of treatments (a.k.a., treatment policies or Just-in-Time Adaptive Interventions) delivered by mobile devices. This is an area of Precision Medicine. I develop new clinical trial designs (designs in which each person is randomized 100s or 1000s of times) and generalize reinforcement learning algorithms to analyze the data and construct treatment policies. I also generalize data analytic method from causal inference for use in analyzing mobile health data.
My research focus is Machine Learning, and I like to explore connections between Optimization, Statistics, and Economics. Increasingly we use ML technologies in which the required assumptions do not apply; for example, when the data are collected in an adaptive fashion from self-interested agents. In such scenarios one must work with new tools that incorporate incentives and strategic behavior.