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Todd Chapman

Todd Chapman was awarded a Department of Defense National Defense Science and Engineering Graduate fellowship in Stanford’s Aeronautics and Astronautics department, where his Ph.D. thesis centered on nonlinear model order reduction methods, a field that has the potential to do for engineering optimization and design what machine learning has done for big data. Problems he has worked on include the simulation of planetary atmospheres in support of NASA’s Cassini mission and underbody blast modeling in cooperation with TARDEC. Todd’s current research interests are in fault-tolerant algorithms for distributed and exascale computing and applying optimal control methods for training stabilized neural network architectures. Todd enjoys collecting first editions and eventually getting around to reading them.

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