Associate Professor of Electrical and Computer Engineering
Harvey D. Spangler Faculty Scholar
Faculty Affiliate in Computer Sciences, Mathematics, and Industrial and Systems Engineering
Fellow of the Wisconsin Institutes of Discovery Optimization Group
Optimization Research Consortium
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 neu- roscience, precision agriculture, biochemistry, and atmospheric science.
Additional recent activities
- Co-PI of NSF Institute for Foundations of Data Science (TRIPODS)
- Invited speaker at 3rd International Matheon Conference on Compressed Sensing and its Applications
- Organizing committee member for SIAM Conference on Computational Science and Engineering (CSE19)
- Invited speaker at ACNTW Workshop on Optimization and Machine Learning
- Invited speaker at Big Data and Ecoinformatics in Agricultural Research; video here
- Co-organizer of BIRS-Oaxaca meeting Beyond Convexity: Emerging Challenges in Data Science (17w5159)
- 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
- Technical program co-chair of Sampling Theory and Applications (SaMPTA) Conference, 2017 and 2019
- Panelist for Bascom Hill Society, 2016 Fall Event (video)
- Lecturer at 12th IEEE EMBS International Summer School on Biomedical Imaging
- General co-chair of SIAM Imaging Science Conference, 2016
- Speaker at Oberwolfach workshop on Computationally and Statistically Efficient Inference for Complex Large-scale Data
- General co-chair of University of Wisconsin Neuroimaging, Computational Neuroscience, and Neuroengineering Workshop, 2015
- 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.
Consider the University of Wisconsin
- Systems Information Learning and Optimization
- The Wisconsin Institute for Discovery Optimization Group
- Great research environment
- You get to live in Madison
Rebecca Willett is an associate professor in the Electrical and Computer Engineering Department and Fellow of the Wisconsin Institute of Discovery at the University of Wisconsin-Madison. Previously she held assistant and associate professor positions at Duke University. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005. 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.
Clarification: I am not Rebekah Willett, UW-Madison Library and Information Studies.