F. Baumdicker: Learning from Trees: Modeling, Simulation and Inference of Microbial Genome Evolution

Franz Baumdicker is head of the Independent Research Group “Mathematical and Computational Population Genetics“ and member of the Cluster of Excellence “Machine Learning for Science“. This talk was part of the colloquium of the Cluster of Excellence. Abstract: Population genetics aims to understand how the observed genetic diversity emerged. 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 bacteria. 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, often represented by a tree sequence. Thus independe