I develop statistical methods and theory to study the effects of natural selection and other evolutionary processes on observed DNA sequences. Using machine learning techniques, I plan to approximate high-dimensional genetic relationships between individuals. Using generative modeling, I plan to generate artificial chromosomes that reflect complex patterns indistinguishable from those seen in real data, particularly at immune complexes like the HLA genes. The scientific impact of my work applies to rare and common disease etiology and epidemiology. In all my work, I strive for reproducible experiments, well-documented software development, efficient computation, and interpretable statistical methods.
- Science Mentor: Gideon Bradburd, Ecology and Evolutionary Biology, LSA
- AI Mentor: Jonathan Terhorst, Statistics, LSA
- Research Theme: Evolution (at the genomics level)