My research interests include signal processing, machine learning, and large-scale data science. This work lies at the intersection of high-dimensional statistics, inverse problems in imaging and network science (including compressed sensing), learning theory, algebraic geometry, statistical signal processing, and optimization theory. My group studies the mathematical foundations of signal processing and machine learning and in their application to a variety of real-world problems with collaborations in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.

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