My interests focus on computational approaches for elucidating mutational processes that generate genetic diversity within populations and across species, with the goal of understanding mechanisms of mutagenesis and DNA repair. I am also interested in developing statistical frameworks for detecting signatures of negative or positive selection, which can be challenging to distinguish from the background DNA sequence variability due to heterogeneous local mutation risk. Novel statistical methods, often based on machine learning, can provide better insight into genome evolution resulting from interplay between mutation and selection. The biological questions I addressed include learning about evolution of gene function and regulation, related to mechanisms underlying genetic disease, and cancer evolution in particular. In addition, I am interested in distributions of local mutation rates in the human germline and somatic genomes, which reveal how DNA repair is organized along chromatin.