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Rebecca Willett, Adjunct Associate Professor of Electrical and Computer Engineering


Rebecca Willett

Rebecca Willett is an assistant professor in the Electrical and Computer Engineering Department 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, 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. 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 Healthcare in 2002. Her research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. Additional information, including publications and software, are available online at

Contact Info:
Office Location:  130 Hudson Hall, Durham, NC 27243
Office Phone:  (608) 833-3375
Email Address: send me a message
Web Page:


Ph.D.Rice University2005
MSRice University2002
B.S.E.E.Duke University2000


Sensing and Sensor Systems
Homeland Security
Medical Imaging
K-12 Education in Science & Mathematics
Signal Processing
Distributed Systems

Research Interests: Networking and Imaging Science

Current projects: Compressive Optical Sensor Design, Anomaly Detection in Sensor Networks, Activity Detection in fMRI, Hyperspectral Image Reconstruction for Astronomy and Multiphoton Microscopy

As the prevalence of sophisticated and inexpensive data collection technology increases, so does our need for accurate and efficient data transmission, storage, analysis, and interpretation. Critical applications such as medical imaging, astrophysics, bioinformatics, communication networks, data mining, and pattern recognition all hinge on our ability to process very large collections of data. The extraction of useful information from data which may be distorted, error riddled, corrupted, or partially irrelevant, or "information processing", is a fundamental challenge faced by diverse fields, from engineering and homeland security to advertising, search engine development, and environmental monitoring. Both my teaching and research are focused on fundamental methodological and theoretical aspects of information processing with a wide variety of important and exciting applications.

Areas of Interest:

Compressed Sensing
Photon-Limited Imaging
Network Anomaly Detection
Medical Imaging
Hyperspectral Imaging
Astronomical Signal Processing


Algorithms • Artifacts • Artificial Intelligence • astronomical signal processing • Big data • compressed sensing • Computer Simulation • fMRI • Image Interpretation, Computer-Assisted • image processing • Image reconstruction • Machine learning • Markov processes • Models, Theoretical • multiscale analysis • optical sensors • Pattern Recognition, Automated • poisson • Reproducibility of Results • Sensitivity and Specificity • signal processing • spectroscopy • superresolution • Video Recording • wavelets

Curriculum Vitae

Current Ph.D. Students  

Postdocs Mentored

Recent Publications   (More Publications)   (search)

  1. Mueller, JL; Fu, HL; Mito, JK; Whitley, MJ; Chitalia, R; Erkanli, A; Dodd, L; Cardona, DM; Geradts, J; Willett, RM; Kirsch, DG; Ramanujam, N, A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins., International Journal of Cancer, vol. 137 no. 10 (November, 2015), pp. 2403-2412, ISSN 0020-7136 [doi]  [abs]
  2. Routtenberg, T; Xie, Y; Willett, RM; Tong, L, PMU-Based Detection of Imbalance in Three-Phase Power Systems, IEEE Transactions on Power Systems, vol. 30 no. 4 (July, 2015), pp. 1966-1976, ISSN 0885-8950 [doi]
  3. Hall, EC; Willett, RM, Online Convex Optimization in Dynamic Environments, IEEE Journal of Selected Topics in Signal Processing, vol. 9 no. 4 (June, 2015), pp. 647-662, ISSN 1932-4553 [doi]
  4. Hall, EC; Willett, RM, Online learning of neural network structure from spike trains, International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering, vol. 2015-July (January, 2015), pp. 930-933, ISSN 1948-3546, ISBN 9781467363891 [doi]  [abs]
  5. Oh, AK; Harmany, ZT; Willett, RM, To e or not to e in poisson image reconstruction, 2014 IEEE International Conference on Image Processing, ICIP 2014 (2015), pp. 2829-2833 [doi]  [abs]