Galen Reeves

Postdoctoral Associate
Department of Statistics
Stanford University,
greeves at stanford dot edu

Brief Bio

I am a Postdoctoral Associate in the Statistics Department at Stanford University. I work with Prof. David Donoho, and I am supported by a NSF VIGRE grant.

I received my Ph.D. in Electrical Engineering from University of California, Berkeley in 2011. My advisor was Michael Gastpar. In the summer of 2011 I was a postdoctoral researcher at EPFL, Switzerland; in the spring of 2009, I was a visiting scholar at the Technical University of Delft, The Netherlands; and in the summer of 2008, I was a research intern at Microsoft Research, Redmond. I received my M.S. in Electrical Engineering from UC Berkeley in 2007, and I received my BS in Electrical and Computer Engineering from Cornell University in 2005.

Research Interests

My areas of research include signal processing, information theory, statistics, and communication. Currently, I am interested in using theoretical tools from these areas to understand the fundamental limitations of, as well as design practical solutions for, various high-dimensional data problems inspired by real-world applications. Some examples of these applications include compressed sensing, sensor networks, massive data storage and retrieval, neuroscience, medical imaging, machine learning and image processing.

Presentations

• G. Reeves and M. Gastpar The Role of Diversity in Sparsity Estimation, Information Theory and Applications Workshop, UCSD, Feb, 2011. (see a three minute presentation on youtube)

Publications (Updated January, 2012)

• G. Reeves, N. Goela, N. Milosavljevic, and M. Gastpar, A Compressed Sensing Wire-Tap Channel, Proceedings of the IEEE Information Theory Workshop (ITW 2011), Paraty, Brazil, October 2011.
  
• G. Reeves and M. Gastpar, On the Role of Diversity in Sparsity Estimation, Proceedings of the IEEE International Symposium on Information Theory (ISIT 2010), Saint Petersburg, Russia, August 2011.
  
• G. Reeves and M. Gastpar, Fundamental Tradeoffs for Sparsity Pattern Recovery, arXiv:1006.3128v1 [cs.IT], June 2010.
• G. Reeves and M. Gastpar, Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds, arXiv:1002.4458v1 [cs.IT], February 2010.
  
• G. Reeves and M. Gastpar, "Compressed" Compressed Sensing, Proceedings of the IEEE International Symposium on Information Theory (ISIT 2010), Austin, Texas, June 2010.
  
• G. Reeves and M. Gastpar, A Note on Optimal Support Recovery in Compressed Sensing, Proceedings of 43-rd Annual IEEE Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, November 2009.
  
• G. Reeves, J. Liu, S. Nath, and F. Zhao, Managing Massive Time Series Streams with Multi-Scale Compressed Trickles, Proceedings of the 35-th International Conference on Very Large Data Bases (VLDB 2009), Lyon, France, August 2009.
  
• G. Reeves and M. Gastpar, Efficient Sparsity Pattern Recovery, Proceedings of the 30-th Symposium on Information Theory in the Benelux, Eindhoven, The Netherlands, May 2009.
  
• G. Reeves and M. Gastpar, Sampling Bounds for Sparse Support Recovery in the Presence of Noise, Proceedings of the IEEE International Symposium on Information Theory (ISIT 2008), Toronto, Canada, July 2008.
  
• G. Reeves, Sparse Signal Sampling using Noisy Linear Projections, Master's Thesis, Dec 2007.
  
• G. Reeves and M. Gastpar, Differences between Observation and Sampling Error in Sparse Signal Reconstruction , Proceedings of the 2007 IEEE Workshop on Statistical Signal Processing (SSP 2007), Madison, Wisconsin, August, 2007.
  
• T. Berger, C. Levy, and G. Reeves, Energy-Efficient Recursive Estimation by Variable Threshold Neurons, Presented at CoSyNe Workshop on Info-Neuro, Park City, UT, February, 2007.
  

Thesis

• G. Reeves Sparsity Pattern Recovery in Compressed Sensing, Ph.D. Thesis, Dec 2011.