statistical machine learning, statistics, Optimization and algorithms, artificial intelligence
In broad terms, I am interested in problems at the interface between computation and statistics. Part of my research focuses on algorithms and Markov random fields, a class of probabilistic model based on graphs used to capture dependencies in multivariate data (e.g., image models, data compression, computational biology). In general, exact solutions to inference problems in such models are computationally intractable, so that there is a great deal of interest in approximate methods for statistical inference. I am also interested in studying the effect of decentralization and communication constraints in statistical inference problems. A final area of interest is methodology and theory for high-dimensional inference problems, in which the model dimension is of the same order (or larger than) the sample size.