I have a background in computer science, artificial intelligence, and theory of computation. After spending some time working in industry I grew tired of solving routine ‘mechanical’ IT problems and decided to return to academia to pursue my PhD in evolutionary biology, under the assumption that evolution, as a highly stochastic and contingent process, would provide more interesting challenges. Turns out I was right on one front, but wrong on another. Evolution is indeed characterized by contingency and stochasticity, but modelling the uncertainty of evolutionary processes is a wildly interesting project, given that theory and modelling are all about developing mechanistic models of non-deterministic processes.
My core research revolves around several themes, the binding element of which is comparative population genetics. One target of my research is to characterize and develop models to explain the distribution of genetic variation in ecological communities. A related focus is to develop models of how biodiversity accumulates in ecological communites that integrate over multiple levels of biological organization, including species abundances, genetic diversities, and phylogenetic relationships. Expanding beyond individual communities, I am also interested in constructing machine learning models which use observed community-level data along with environmental data to identify environmental correlates of commuity abundance and genetic diversity structure. Finally, I construct inferential frameworks utilizing whole-genome data to understand how geographically co-distributed taxa have or have not concordantly responded to fluxuating shared environmental conditions.