Jonathan Terhorst

Jonathan Terhorst is an Associate Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics within the College of Literature, Science, and the Arts. He holds a Ph.D. in Statistics from UC Berkeley (2017) and joined the University of Michigan faculty in 2017. His research focuses on applying statistical and machine learning methods to problems in genetics and population biology, particularly in developing computational tools for analyzing genomic data.

His work emphasizes demographic inference, phylogenetic modeling, and the application of advanced statistical techniques to understand human and microbial population histories. Notable contributions include methodologies for decoding coalescent models, analyzing allele frequency spectra, and detecting natural selection in ancient and modern populations. Terhorst’s research often bridges theoretical statistics with practical computational challenges in large-scale genomic datasets.

He oversees a lab dedicated to advancing statistical genetics, as reflected in his Lab Web Site. While specific student advisees are not listed here, his academic role implies active mentorship in the Ph.D. and Master's programs. His work has been recognized through collaborations in high-impact studies, such as the analysis of Bronze Age British populations and African demographic histories.

Terhorst’s publications span topics from coalescent theory to scalable algorithms for genomic data, reflecting a commitment to both foundational and applied research in population genetics. His research addresses questions about human migration, evolutionary processes, and the statistical underpinnings of modern genetic inference.