I am a Postdoctoral Associate in the Statistics Department at Stanford University. I work with 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, where I worked with Jie Liu in the Networked Embedded Computing Group. 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.

My research interests are in signal processing, statistics, and information theory, with applications in compressed sensing, massive data storage and retrieval, neuroscience, and machine learning. My Ph.D. dissertation used tools from information theory to provided a sharp characterization of the problem of sparsity pattern recovery in compressed sensing.

• The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing G. Reeves and M. Gastpar, IEEE Transactions on Information Theory, vo. 58, no. 10, pp. 3065-3092, May, 2012. [arxiv]
• Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds G. Reeves and M. Gastpar, to appear in the IEEE Transactions on Information Theory.

• The Sensitivity of Compressed Sensing Performance to Relaxation of Sparsity D. L. Donoho and G. Reeves, Proceedings of the IEEE International Symposium on Information Theory (ISIT 2012), Boston, MA, July 2012.
• Compressed Sensing Phase Transitions: Rigorous Bounds versus Replica Predictions G. Reeves and M. Gastpar, Proceedings of the 46-th Annual Conference on Information Sciences and Systems (CISS 2012), Princeton, NJ, Mar 2012.
• A Compressed Sensing Wire-Tap Channel G. Reeves, N. Goela, N. Milosavljevic, and M. Gastpar, Proceedings of the IEEE Information Theory Workshop (ITW 2011), Paraty, Brazil, October 2011.
• On the Role of Diversity in Sparsity Estimation G. Reeves and M. Gastpar, Proceedings of the IEEE International Symposium on Information Theory (ISIT 2011), Saint Petersburg, Russia, August 2011. [See a short presentation on youtube]
• "Compressed" Compressed Sensing G. Reeves and M. Gastpar, Proceedings of the IEEE International Symposium on Information Theory (ISIT 2010), Austin, TX, June 2010.
• A Note on Optimal Support Recovery in Compressed Sensing G. Reeves and M. Gastpar, Proceedings of 43-rd Annual IEEE Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, November 2009.
• Managing Massive Time Series Streams with Multi-Scale Compressed Trickles G. Reeves, J. Liu, S. Nath, and F. Zhao, Proceedings of the 35-th International Conference on Very Large Data Bases (VLDB 2009), Lyon, France, August 2009.
• Efficient Sparsity Pattern Recovery G. Reeves and M. Gastpar, Proceedings of the 30-th Symposium on Information Theory in the Benelux, Eindhoven, The Netherlands, May 2009.
• Sampling Bounds for Sparse Support Recovery in the Presence of Noise G. Reeves and M. Gastpar, Proceedings of the IEEE International Symposium on Information Theory (ISIT 2008), Toronto, Canada, July 2008.
• Differences between Observation and Sampling Error in Sparse Signal Reconstruction G. Reeves and M. Gastpar, Proceedings of the 2007 IEEE Workshop on Statistical Signal Processing (SSP 2007), Madison, Wisconsin, August, 2007.
• Energy-Efficient Recursive Estimation by Variable Threshold Neurons T. Berger, C. Levy, and G. Reeves, Presented at CoSyNe Workshop on Info-Neuro, Park City, UT, February, 2007.

• Sparsity Pattern Recovery in Compressed Sensing G. Reeves, Ph.D. Thesis, Dec 2011.
• Sparse Signal Sampling using Noisy Linear Projections G. Reeves, Master's Thesis, Dec 2007.

• STATS 110 - Statistical Methods in Engineering and the Physical Sciences, Autumn, 2011 and 2012.
• STATS 60 - Introduction to Statistical Methods: Precalculus, Spring, 2012.