Navid Dianati is a Senior Applied Scientist in Seattle with 11 years of experience translating advanced math and physics into production-ready machine learning and data analytics for scientific discovery. With a PhD in physics and an MS in applied mathematics from the University of Michigan, he has driven cross-disciplinary projects from computational social science to large-scale drug discovery, leading the analytical phase of a Broad–Bayer partnership that triaged 100K compounds. He combines a strong theoretical background—publishing novel statistical methods—with hands-on engineering of data pipelines, anomaly detection, metric learning, and deep learning for high-throughput genomics. Comfortable moving between research and product, he excels at turning complex experimental data into mechanistic hypotheses and prioritized candidates for follow-up. Outside work he pursues algorithms, mathematical digital art and photography, reflecting a curiosity that fuels both technical depth and creative problem-solving.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Physics, Doctor of Philosophy (Ph.D.), Physics at University of Michigan
Package for "pruning" weighted complex networks based on the Marginal Likelihood Filter.
Contributions:1 release, 30 commits, 20 pushes in 6 years 3 months
marginalpruningcomplex-networkslikelihoodjulia
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Navid Dianati - Senior Applied Scientist at Amazon