“Big Data” is transforming our understanding of genetic variation across chromosomes, individuals, populations, and species. Our research group seeks to understand these drivers of this variation using new statistical frameworks and computational algorithms for solving big questions in biology using diverse “omics”-scale data. A major goal is to illuminate drivers of genomic novelty, innovation, and functional coevolution of quantitative traits (e.g., gene expression) in agricultural pests (e.g., herbivorous weevils). To tackle these questions, we develop efficient and effective statistical frameworks for deriving meaningful conclusions and predictions about processes that generate genomic diversity. Our work seeks to build, test, and inspire new statistical frameworks & algorithms for asking big questions using “Big Data” in genomics; through this goal, we target fundamental hypotheses about drivers of quantitative trait evolution, population genomics, and functional genetics.

I am a broadly trained statistical geneticist and data scientist with research in statistical population genetics, mathematical & algorithmic phylogenomics, computational/functional genomics, and artificial intelligence-driven applications in genetics.