I’m a computational biologist with focus on Bayesian phylogenetic inference. Natural selection by means of adaptation and genetic inheritance are key principles in evolutionary biology. Evolutionary relationships are commonly depicted by phylogenetic trees. Using phylogenetic methods we can learn about the evolutionary history of species and the processes that have contributed to present diversity. I’m developing statistical and computational methods to infer phylogenies from molecular sequence data. These methods additionally identify periods of adaptive genetic evolution at lineage or genes. Furthermore, I develop mathematical models to study macroevolutionary patterns, such as, diversification rate variation over time and among lineages and episodes of global mass extinctions.
I did my undergraduate degree (Diplom-Ingenieur) at The Berlin School of Economics and Law (Berlin, Germany) in technical engineering/informatics. During this time I worked as a software developer for Schering AG (pharmaceutics). Afterwards I obtained a Master’s degree in Computer Science at The University of Auckland (Auckland, New Zealand) under the supervision of Alexei Drummond. My focus during my Master studies was on efficient algorithms for inferring phylogenetic trees using Bayesian Markov chain Monte Carlo simulations. Next, I obtained my PhD degree in Mathematical Statistics from the Stockholm University (Stockholm, Sweden) under the supervision of Tom Britton and Fredrik Ronquist. In Stockholm I worked on birth-death processes for estimating diversification rates and started the development of
RevBayes, a software for Bayesian inference of phylogeny. Currently I am a Miller Fellow at the University of California, Berkeley (USA) in the Huelsenbeck lab and Nielsen lab.