About

I started in applied mathematics and moved toward biology because that is where some of the most interesting modelling problems live. Today I work at the intersection of the two: using statistics and code to make sense of genetic data.

My master’s research benchmarks kinship software under extreme demographic scenarios — stress-testing the tools other researchers rely on, so their conclusions hold up. Earlier, my undergraduate thesis tested admixture-inference software across diverse demographic models. Both share a theme I care about: knowing not just what a method outputs, but when it can be trusted.

Alongside research I build practical tools, from machine-learning pipelines to alignment-free sequence comparison. I like work that is reproducible, well-reasoned and honest about its uncertainty.