Octant
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.
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Octant
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Octant is a well-backed team of experienced scientists and entrepreneurs exploring new frontiers of biology. We are applying next-generation DNA sequencing, gene synthesis, and gene editing to revolutionize drug discovery in a quest for safer, more effective, and cheaper drugs.