Rebecca Willett

Rebecca Willett
Machine learning and signal processing theory, methodology, and applications.

Years at WID

2013 - present2018


Rebecca Willett is an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institute for Discovery at the University of Wisconsin-Madison. 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. 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.


  • B.S.E., Electrical and Computer Engineering Distinction and Summa Cum Laude, Duke University
  • M.S., Electrical and Computer Engineering, Rice University
  • Ph.D., Electrical and Computer Engineering, Rice University

Research Description

Rebecca Willett's research is focused on developing machine learning and signal processing theory and methodology that exploit underlying low-dimensional models, including sparse and low-rank representations of data. 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. 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. 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 active collaborations with researchers in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.


  • Affiliate appointment in Computer Sciences
  • Affiliate appointment in Mathematics
  • Affiliate appointment in Industrial and Systems Engineering
  • Senior Member of IEEE


  • Harvey D. Spangler Faculty Scholar
  • AFOSR Young Investigator Program Award Recipient
  • NSF CAREER Award Recipient

Selected Publications

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