Contact Info
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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. Her research is focused on machine learning, signal processing, and large-scale data science. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018. Willett received the National Science Foundation CAREER Award in 2007, is a member of the DARPA Computer Science Study Group, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Willett has also held visiting researcher or faculty positions at the University of Nice in 2015, the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Healthcare in 2002.


Focus Areas: Signal Processing, Machine Learning, Data Science

My research interests include signal processing, machine learning, and large-scale data science. In particular, I have 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. This work lies at the intersection of 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. My group has made contributions both in the mathematical foundations of signal processing and machine learning and in their application to a variety of real-world problems. I have active collaborations with researchers in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.


AI & Machine Learning

Foundations and applications of computer algorithms making data-centric models, predictions, and decisions

Data & Databases

Systems and algorithms for managing and analyzing data at scale

Scientific & High Performance Computing

Scientific discovery at the frontiers of computational performance, intelligence, and scale

Labs & Groups

Machine Learning Group

Researchers at the University of Chicago and partner institutions studying the foundations and applications of machine learning and AI.

CERES Center for Unstoppable Computing

Andrew A. Chien
A dynamic community focused on reducing the fragility and complexity of computing systems, while also increasing their efficiency and lifetime.

News & Events

UChicago CS News

UChicago London Colloquium Features Data Science, Quantum Research

Jul 01, 2022
UChicago CS News

Prof. Rebecca Willett Named IEEE Fellow

Nov 29, 2021
UChicago CS News

UChicago and EPiQC Alum Yongshan Ding Joins Yale in Faculty Position

Oct 08, 2021
UChicago CS News

$3.25m DOE Grant Funds UChicago/Argonne Research on AI Models of Physics Simulations

Sep 27, 2021
UChicago CS News

DSI Discovery Challenge Awardees Train Data Science on Medicine, Clean Water, and Education

Apr 09, 2021
UChicago CS News

Prof. Rebecca Willett Named SIAM Fellow

Mar 31, 2021
UChicago CS News

UChicago CS Alums Go On To Exciting Futures in Academia, Industry, and Startups

Nov 24, 2020
UChicago CS News

UChicago Joins Three Universities in Institute for Foundational Data Science

Sep 01, 2020
UChicago CS News

New AI + Science Grants Fund Projects & Workshops in Chemistry, Physics, and CS Education

May 12, 2020
Past Event

Ashia Wilson (Microsoft Research) – Variational Perspectives on Machine Learning: Algorithms, Inference, and Fairness

Mar 04, 2020
Past Event

Rebecca Willett (UChicago) – Leveraging Physical Models in Machine Learning

Computational Science Seminar sponsored jointly by the University of Chicago, Argonne National Laboratory and Fermilab
Dec 12, 2019
Past Event

Ramya Vinayak (Washington) – Learning From Sparse Data

Nov 22, 2019
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