Rebecca Willett

Rebecca Willett

Rebecca Willett

Discovery Fellow, Associate Professor, Electrical and Computer Engineering, Optimization


  • B.S.E., Electrical and Computer Engineering, Duke University
  • M.S., Electrical and Computer Engineering, Rice University
  • Ph.D., Electrical and Computer Engineering, Rice University

Research Description

Rebecca Willett’s research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. One central theme of her 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.

Selected Publications

  • M. Raginsky, R. Willett, Z. Harmany, and R. Marcia, “Compressed sensing performance bounds under Poisson noise,” IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 3990–4002, arXiv:0910.5146, 2010.
  • J. Silva and R. Willett, “Hypergraph-based anomaly detection in very large networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 563–569, doi:10.1109/TPAMI.2008.232, 2009.
  • Z. Harmany, R. Marcia, and R. Willett, “Spatio-temporal compressed sensing with coded apertures and keyed exposures,” submitted, based on 2008 EUSIPCO paper “Compressive coded aperture video reconstruction”. arXiv:1111.7247, 2011.
  • K. Krishnamurthy, M. Raginsky, and R. Willett, “Multiscale photon-limited spectral image reconstruction,” SIAM Journal on Imaging Sciences, vol. 3, no. 3, pp. 619 – 645, doi:10.1137/090756259, 2010.
  • Z. Harmany, R. Marcia, and R. Willett, “This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruc- tion ALgorithms Theory and Practice,” IEEE Transactions on Image Processing, vol. 21, no. 3, doi:10.1109/TIP.2011.2168410, 2012.
  • M. Gehm, R. John, D. Brady, R. Willett, and T. Schultz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Optics Express, vol. 15, no. 21, pp. 14013–14027, doi:10.1364/OE.15.014013, 2007.
  • M. Raginsky, R. Willett, C. Horn, J. Silva, and R. Marcia, “Sequential anomaly detection in the presence of noise and limited feedback,” IEEE Transactions on Information Theory, vol. 58, no. 8, pp. 5544–5562, doi:10.1109/TIT.2012.2201375, 2012.
  • E. Arias-Castro, J. Salmon, and R. Willett, “Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods,” SIAM Journal on Imaging Sciences, vol. 5, no. 3, pp. 944–992, doi:10.1137/110859403, 2012.