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.
| Office Location: | FCIEMAS 3463 |
| Office Phone: | (919) 660-5544 |
| Email Address: | |
| Web Page: | http://www.ee.duke.edu/~willett |
| Ph.D. | Rice University | 2005 |
| MS | Rice University | 2002 |
| B.S.E.E. | Duke University | 2000 |
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.