Computational Biology, with an emphasis on developing formal models and algorithms that address problems of practical biological and medical concerns, such as, 1) decoding transcriptional regulation networks of higher eukaryotic organisms via joint analysis of genomic, proteomic and developmental data; 2) statistical inference of haplotypes, linkage and pedigree for genetic, clinical and forensic applications; 3) stochastic and game-theoretic models for evolution, immune response and oncogenesis; and 4) modeling substitution, recombination, selection and genome rearrangement for comparative genomic analysis.

Statistical Machine Learning, emphasizing theory and algorithms for learning complex probabilistic models, learning with prior knowledge, and reasoning under uncertainty. We focus on, 1) variational inference/learning theory and development of turn-key variational inference engines; 2) Nonparametric Bayesian analysis, algorithms and applications of Bayesian nonparametrics in data mining; and 3) Probabilistic and optimization-theoretic methods for semi-unsupervised learning,  and learning with kernel machines. Also of interest include graphical game theory and applications.