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Moses Chan is currently an Assistant Professor of Instruction in the Department of Industrial Engineering and Management Sciences at Northwestern University. He teaches courses focusing on interface statistics, data science, and engineering, aiming to connect students with real-world decision-making through the application of statistical reasoning. His current research investigates the design of computational methods for high-dimensional statistical analysis, particularly in the physical sciences. He has a specific interest in the development of Gaussian process methods for Bayesian emulation and calibration problems. Additionally, Chan is affiliated with the Bayesian Analysis for Nuclear Dynamics (BAND) collaboration framework. Before joining Northwestern, he worked at the Center for Healthcare Engineering and Patient Safety (CHEPS) at the University of Michigan, where he contributed to various projects aimed at assessing and improving patient outcomes.
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