Mathematical and Computational Population Genetics

Research Interests

Our research focuses on mathematical models describing the evolution and ecology of microbial genes and genomes. In population genetics, many theoretical results have been developed in times where not much genomic and genetic data were available. These theory-driven results are still essential for our research, but data-driven discoveries have meanwhile dramatically changed our view of evolution and ecology, in particular for prokaryotes. Today we can observe low frequency variations in genomic data and sequence the genomes of thousands of individuals, even at the single cell level.
We work at the interface between these two worlds and combine the areas of mathematical population genetics, computational biology, and machine learning. We aim to understand how the observed diversity of microbes emerged. The vast amount of newly sequenced genetic data leads to a multitude of interesting applications in the emerging field of machine learning in population genetics. The main challenge is that sequence data are not independent of one another, but rather are linked by their phylogenetic relationship. Our goal is to develop, analyze, and apply supervised machine learning tools that can use this phylogenetic relationship to improve our understanding of bacterial genome evolution and human population history.