Advancing Evolutionary Inference with Machine Learning and Population Genetic Simulation

Since its inception the Schrider Lab has worked to adapt powerful machine learning methods to all manner of questions in evolutionary inference, but with a particular focus on detecting the population genetic signatures of adaptation. More recently, we have branched out to using population genetic simulations to study topics as diverse as the optimization of cancer treatment strategies and the evolution of polyploidy. We also conduct empirical studies in the lab, which are currently focused on natural selection, demographic change, and genomic structural variation in mosquito populations.