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Wei-Biao Wu is a Professor in the Department of Statistics at the University of Chicago. His research focuses on high-dimensional inference, where he investigates the framework and systematic theory of high-dimensional inference under dependence. Wu develops necessary tools for model selection, covariance matrix estimation, regression, mean vector estimation, and multiple testing problems, particularly when data dependence is a factor. He also explores the probabilistic aspects, including deviation concentration inequalities of dependent random variables and the exponential moments. Additionally, Wu is interested in the deep Gaussian approximation problem and aims to establish robust methodologies in high-dimensional settings.
University of Chicago • Chicago, IL
Teaching and research in high-dimensional statistics and related fields.
Department of Philosophy