Spatial population genetics

Geography a huge impact on how genetic variation is shared within populations. This can confound standard population genetics methods, but also provides another axis of information we can use to understand the evolutionary processes behind genetic data. Right now, I’m using recent developments in continuous-space simulation to model how spatial population structure affects the dynamics of natural selection. With a better understanding of how spatial processes impact patterns of variation, we can understand more about the evolutionary past - and present - of a population!

Machine learning inference for population genetics

AI might not replace artists, but it could be good for looking at genomes - applying machine learning methods to population genetic datasets is just as, if not more useful than standard methods. I’m interested in incorporating spatial data into our methods of machine learning on genomes and uncovering how this dimension of data can help in idenfitying evolutionary processes. Population genetic data isn’t without bias, however, and I’m also focused on how our approaches to building ML models and analyzing datasets can impact our results.

Host-parasite coevolution

I have a soft spot for infectious disease, particulary zoonotic and vector-borne pathogens. My current research is all based in simulation, inference, and theory, but I focus on applying my findings to the malaria vector Anopheles gambiae and its parasite Plasmodium falciparum. I’m always looking for more chances to investigate other disease systems as well!