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James G. Scott joined the University of Texas at Austin in 2009. He has made foundational contributions in Bayesian methods for high-dimensional data, particularly in areas such as sparsity, regularization, multiple testing, and Bayesian computation. His recent work has focused on pressing public health policy questions, pioneering studies on access to abortion medication via telemedicine, and the creation of real-time disease tracking and forecasting models during the COVID-19 pandemic. His current research concerns statistical reliability in modern generative machine learning models for scientific inference, specifically concentrating on methods for neural posterior estimation. He received the UT System Regents’ Outstanding Teaching Award in 2014, among other teaching awards.
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