Optimizing Genomic Sampling to Learn the Evolutionary History of Pathogen Populations

David Rasmussen leads the Phylodynamics Research Group at NC State, which focuses on developing new computational and statistical methods for genomic epidemiology, population genomics and phylogenetics. Much of our research focuses on developing "phylodynamic" methods to better understand the evolution of microbial pathogens by combining population dynamic modeling with phylogenetic methods. We have applied these approaches to RNA viruses and bacteria to explore the genomic determinants of pathogen fitness in real world environments and to identify potential fitness trade-offs between alternate environments. Recent work has also focused on optimizing sampling designs for demographic inference from population genomic data. Framing genomic sampling as a sequential decision making problem (i.e. a Markov decision process), allows us to maximize the information gained from genomic data about a population's history using classic dynamic programming and modern reinforcement learning methods.