Rebecca Willett is a professor of statistics and computer science at the University of Chicago. Her research interests include signal processing, machine learning, and large-scale data science. In particular, she has studied methods to leverage low-dimensional models in a variety of contexts, including when data are high-dimensional, contain missing entries, are subject to constrained sensing or communication resources, correspond to point processes, or arise in ill-conditioned inverse problems.
Prof. Willett’s lab performs research in machine learning and signal processing and has made contributions both in the mathematical foundations of machine learning and in the application of those methods and tools to a variety of real-world contexts. Including active collaborations with researchers in astronomy, heliophysics, genomics, materials science, statistics, microscopy, virology, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry and atmospheric science.
Prof. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group 2007-2011 and received an Air Force Office of Scientific Research Young Investigator Program award in 2010.