My research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. One central theme of my research is data-starved inference for point processes -- the development of statistically robust methods for analyzing discrete events, where the discrete events can range from photons hitting a detector in an imaging system to groups of people meeting in a social network. When the number of observed events is very small, accurately extracting knowledge from this data is a challenging task requiring the development of both new computational methods and novel theoretical analysis frameworks. This body of research has led to important insights into the performance of compressed sensing in optical systems, tools for tracking dynamic meeting patterns in social network, and novel sparse Poisson intensity reconstruction algorithms for night vision and medical imaging.
Additional recent activities
- General co-chair of SIAM Imaging Science Conference, 2016
- General co-chair of University of Wisconsin Neuroimaging, Computational Neuroscience, and Neuroengineering Workshop, 2015
- Keynote speaker at IEEE GlobalSIP, 2014
- Grossman Center for the Statistics of Mind, 2014
- IMA-HK-IAS Joint Program on Statistics and Computational Interface to Big Data, 2015
- 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.
- Invited speaker at Matheon Workshop on Compressed Sensing and Its Applications 2013.
- GlobalSIP mini symposium on “Emerging Challenges in Network Sensing, Inference, and Communication” technical chair 2013.
- Asilomar track chair 2013.
- Information Initiative at Duke, associate director 2013-present.
- 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.