Nathan is a computational geneticist and a member of the Octant Compute team. He works on the design and statistical interpretation of multiplexed, high-throughput functional assays of human genetic variation. His other research interests include statistical human genetics, RNA-mediated post-transcriptional gene regulation and applied machine learning in molecular biology. He obtained his PhD in Genetics and MS in Statistics from Stanford University, and his BS in Molecular Biology and BA in Political Science from the University of Texas at Austin. In a past life, he contributed to empirical studies in comparative constitutional design and participated in/coached high school debate for many years.