معرفی
Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago and Faculty Director of AI at the Data Science Institute. She holds the Worah Family Professorship and is a member of the Wallman Society of Fellows. Her research focuses on machine learning, signal processing, and scientific computing, with applications in astronomy, climate science, and biochemistry. She has held visiting roles at institutions including UCLA and INRIA. Key roles include Deputy Directorships at the NSF-Simons Institute for Theory and Mathematics in Biology and the SkAI Institute.
Education: PhD in Electrical and Computer Engineering from Rice University (2005), followed by faculty roles at Duke University (2005–2013) and the University of Wisconsin-Madison (2013–2018). Awards include the 2024 SIAM Data Science Career Award, NSF CAREER Award (2007), and AFOSR Young Investigator Award (2010).
Research interests span inverse problems, optimization theory, and interdisciplinary applications. Her work bridges high-dimensional statistics and imaging science. Recent articles emphasize neural network theory, climate data assimilation, and biophysical modeling.
Awards include SIAM Fellowship, IEEE Fellowship, and teaching excellence awards. She leads initiatives in AI ethics, broadening participation in STEM, and serves on key committees like the National Academies' CATS.
Labs/Groups: Machine Learning Group at UChicago, CERES Center for Unstoppable Computing. Grants include NSF, DOE, and collaborations with Argonne National Laboratory.
