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Rebecca Willett is a Professor in the Department of Statistics at the University of Chicago. Her research interests include signal processing, machine learning, and large-scale data science. She has studied methods that leverage low-dimensional models in various contexts, particularly in high-dimensional data that contain missing entries and are constrained by sensing and communication resources. Her work intersects high-dimensional statistics, inverse problems in imaging and network science (including compressed sensing), learning theory, algebraic geometry, optical engineering, nonlinear approximation theory, statistical signal processing, and optimization theory. Willett's group has made significant contributions to the mathematical foundations of signal processing and machine learning, applying these principles to solve a variety of real-world problems. She is involved in active collaborations with researchers in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.
Department of Philosophy