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.
Upcoming or recent activities
- Keynote speaker at Biological and Astronomical Signal Processing (BASP) Frontiers workshop, 2019
- Co-Chair of Technical Committee for IEEE Data Science Workshop, EEE Data Science Workshop (DSW), Minneapolis, June 2-5, 2019
- Member of Organizing Committee for SIAM CSE19
- Invited talk at Institut Henri Poincaré workshop on variational
methods and optimization in imaging
- Co-PI on new Air Force Center of Excellence on Efficient and Robust Machine Learning
- Recipient of the 2018 Gerald Holdridge Teaching Excellence Award
- 5-minute general audience talk at Wisconsin Science Festival
- Interviewed on Wisconsin Public Radio
- Speaker at Wisconsin Science Festival
- Co-PI of NSF Institute for Foundations of Data Science (TRIPODS)
- Invited speaker at 3rd International Matheon Conference on Compressed Sensing and its Applications
- Invited speaker at ACNTW Workshop on Optimization and Machine Learning
- Invited speaker at Big Data and Ecoinformatics in Agricultural Research; video here
- Invited speaker at SIAM Annual Meeting 2017
- Invited speaker at learning theory workshop at FoCM, 2017
- Invited speaker at 61st World Statistics Congress — ISI2017
- Plenary speaker at SPARS 2017
- Panelist for Bascom Hill Society, 2016 Fall Event (video)
- Lecturer at 12th IEEE EMBS International Summer School on Biomedical Imaging
- Speaker at Oberwolfach workshop on Computationally and Statistically Efficient Inference for Complex Large-scale Data
- Keynote speaker at IEEE GlobalSIP, 2014
- Keynote speaker at SPIE Wavelets and Sparsity XVI, 2015
- Elected as SIAM Imaging Science Program Director, 2014-2016.
- Plenary speaker at SIAM Conference on Imaging Science 2014. Slides here! (Use Adobe Reader to see videos embedded in slides.)
- Keynote speaker at SMAI Curves and Surfaces Workshop 2014.
- Information Initiative at Duke, associate director 2013.
- 2015, 2013, 2011 Duke Workshop on Sensing and Analysis of High-Dimensional Data, co-organizer.
Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. 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. 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. Prof. Willett has also held visiting researcher positions at 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 Medical Systems (now GE Healthcare) in 2002. Her research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. She is also an instructor for FEMMES (Females Excelling More in Math Engineering and Science; news article here) and a local exhibit leader for Sally Ride Festivals. She was a recipient of the National Science Foundation Graduate Research Fellowship, the Rice University Presidential Scholarship, the Society of Women Engineers Caterpillar Scholarship, and the Angier B. Duke Memorial Scholarship.