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Rebecca Willett is a Professor in the Departments of Statistics and Computer Science at the University of Chicago. Her research interests focus on signal processing, machine learning, and large-scale data science, specifically on methods that leverage low-dimensional models in various contexts, including data that is high-dimensional, contains missing entries, and is subject to constrained sensing and communication resources. Her work addresses inverse problems, lies at the intersection of high-dimensional statistics, imaging, and network science (including compressed sensing), learning theory, algebraic geometry, optical engineering, nonlinear approximation theory, and statistical signal processing. Willett has made significant contributions to the mathematical foundations of signal processing and machine learning, applying them to a range of real-world problems. She is actively collaborating with researchers in fields such as astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.
Department of Philosophy