In honor of Mathematics and Statistics Awareness Month, SIAM is spotlighting mathematicians and statisticians throughout April. Dr. Rebecca Willett is a SIAM Class of 2021 Fellow and is a Professor of Statistics and Computer Science at the University of Chicago.
Career and Awards
Dr. Willett completed her Ph.D. 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. She is currently a Professor of Statistics and Computer Science at the University of Chicago.
In 2007, Dr. Willett received the National Science Foundation CAREER Award. She was a member of the DARPA Computer Science Study Group, and in 2010, she received an Air Force Office of Scientific Research Young Investigator Program Award.
Throughout her career, Dr. Willett 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. This work lies at the intersection of high-dimensional statistics, inverse problems in imaging and network science, learning theory, algebraic geometry, optical engineering, nonlinear approximation theory, statistical signal processing, and optimization theory. Her 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. She has worked with collaborators in astronomy, materials science, molecular biology, microscopy, precision agriculture, and atmospheric science.
“I am particularly excited by and curious about several major open questions in machine learning (ML). We are continuously building new ML systems, but these systems do not consistently exhibit efficient use of data and computational resources, robustness to different domains, stability with respect to changes in the input data, or established safety and ethical standards,” says Dr. Willett. “These challenges are highly relevant to a society that is increasingly using ML - in self-driving cars, job searches, criminal justice, finance, and healthcare. There is an enormous opportunity for SIAM members to help build a fundamental mathematical and statistical understanding of efficiency, robustness, stability, and safety in ML.”
Dr. Willett has served as an Associate Editor for SIAM Review, SIAM Journal on Mathematics of Data Science, and SIAM Journal on Imaging Sciences. She served as Co-Chair for the SIAM Conference on Imaging Science (IS16) and on the organizing committee for the SIAM Conference on Computational Science and Engineering (CSE19). Most recently, she was named a SIAM Fellow in the Class of 2021.
She is a co-principal investigator and member of the Executive Committee for the Institute for the Foundations of Data Science, helps direct the Air Force Research Lab University Center of Excellence on Machine Learning, and currently leads the University of Chicago’s AI+Science Initiative. Dr. Willett serves on advisory committees for the National Science Foundation’s Institute for Mathematical and Statistical Innovation, the AI for Science Committee for the U.S. Department of Energy’s Advanced Scientific Computing Research Program, the Sandia National Laboratories Computing and Information Sciences Program, and the University of Tokyo Institute for AI and Beyond.
Thank you, Dr. Willett, for your contributions to SIAM and for your leadership in mathematics and statistics!