Duke Probability Theory and Applications
   Search Help Login Join pdf version printable version

Rebecca Willett, Associate Professor of Electrical & Computer Engineering

Rebecca Willett

Please note: Rebecca has left the "Probability: Theory and Applications" group at Duke University; some info here might not be up to date.

Rebecca Willett completed her PhD in Electrical and Computer Engineering at Rice University in 2005. In addition to studying at Rice, she has worked as a Fellow of the Institute for Pure and Applied Mathematics at UCLA, as a visiting researcher at the University of Wisconsin-Madison and the French National Institute for Research in Computer Science and Control (INRIA), and as a member of the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare). She is the recipient of the National Science Foundation Graduate Research Fellowship , the Rice University Presidential Scholarship, and the Society of Women Engineers Caterpillar Scholarship.

Contact Info:
Office Location:  FCIEMAS 3463
Office Phone:  (919) 660-5544
Email Address: send me a message
Web Page:  http://www.ee.duke.edu/~willett

Education:

PhDRice University2005
MSRice University2002
BSEDuke University2000
Specialties:

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

Keywords:

poisson • wavelets • signal processing • compressed sensing • image processing • multiscale analysis • spectroscopy • optical sensors • superresolution • fMRI • astronomical signal processing

Curriculum Vitae
Current Ph.D. Students  

Postdocs Mentored

Recent Publications   (More Publications)   (search)

  1. E. Wang, J. Silva, R. Willett, and L. Carin, Time-Evolving Modeling of Social Networks, ICASSP (2011)  [abs]
  2. C. Horn and R. Willett, Online anomaly detection with expert system feedback in social networks, ICASSP (2011)  [abs]
  3. R. Willett, Errata: Sampling Trajectories for Sparse Image Recovery (2011)  [abs]
  4. M. Raginsky, N. Kiarashi, and R. Willett, Decentralized online convex programming with local information, Proceedings of American Control Conference (2011)  [abs]
  5. E. Wang, J. Silva, R. Willett, L. Carin, Dynamic Relational Topic Model for Social Network Analysis with Noisy Links, Statistical Signal Processing Workshop 2011 (2011)