Department of Electrical and Computer Engineering
Department of Statistical Science
My research interests lie at the intersection of signal processing, statistics, and information theory, with applications in high-dimensional statistical inference, compressed sensing, and machine learning.
G. Reeves, "Two-Moment Inequalities for Rényi Entropy and Mutual Information," ISIT 2017
A. Kipnis, G. Reeves, Y. C. Eldar, A. Goldsmith, "Compressed Sensing under Optimal Quantization," ISIT 2017
G. Reeves, "Conditional Central Limit Theorems for Gaussian Projections," ISIT 2017
03/2017 -- My work with Efe Aras on modeling traffic with self-driving cars has been featured on the Pratt School of Engineering website.
02/2017 -- I've just uploaded to arxiv my paper Two-Moment Inequalities for Rényi Entropy and Mutual Information. This paper provides a generalization of some of the techniques that were used in my recent work on the conditional central limit theorems for Gaussian projections. In particular, it is shown how mutual information can be upper bounded in terms of weighted integrals involving the variance of the conditional density.
12/2016 -- I've just uploaded to arxiv my paper Conditional Central Limit Theorems for Gaussian Projections. This paper deals with the surprising phenomenon that most projections of a high-dimensional vector are close to Gaussian. Many of the ideas were motivated by my work with Henry Pfsiter on the replica-symmetric prediction for compressed sensing.
V. Mayya, B. Mainsah, and G. Reeves. "Information-Theoretic Analysis of Refractory Effects in the P300 Speller," Asilomar 2016.
V. Mayya, B. Mainsah, and G. Reeves. "Modeling the P300-Based Brain-Computer Interface As a Channel with Memory," Allerton 2016.
08/2016 -- I'm teaching ECE 587 / STA 563 -- Information Theory in Fall 2016
07/2016 -- Henry Pfister and I have just uploaded to the arxiv our paper The Replica-Symmetric Prediction for Compressed Sensing with Gaussian Matrices is Exact. The paper resolves a long-standing open problem about concerning results made using the powerful but heuristic replica methods from statistical physics. The main ideas in the proof are outlined in this video, which I presented at the IHP Nexus of Information and Computation Theories, March 2016.
04/2016 -- Here is as video of an invited talk outlining a rigorous proof of the replica-symmetric prediction for compressed sensing that I presented at the IHP Nexus of Information and Computation Theories, March 2016.
02/2016 -- I'm helping co-organize the 2016 North-American School of Information Theory at Duke University.
01/2016 -- I'm teaching STA 741 / ECE 741 -- Compressed Sensing and Related Topics in Spring 2016.
Galen Reeves joined the faculty at Duke University in Fall 2013, and is currently an Assistant Professor with a joint appointment in the Department of Electrical & Computer Engineering and the Department of Statistical Science. He completed his PhD in Electrical Engineering and Computer Sciences at the University of California, Berkeley in 2011. From 2011 to 2013 he was a postdoctoral associate in the Departments of Statistics at Stanford University, where he was supported by an NSF VIGRE fellowship. In the summer of 2011, he was a postdoctoral researcher in the School of Computer and Communication Sciences at EPFL, Switzerland; in the spring of 2009, he was a visiting scholar at the Technical University of Delft, The Netherlands; and in the summer of 2008, he was a research intern in the Networked Embedded Computing Group at Microsoft Research, Redmond. He received his MS in Electrical Engineering from UC Berkeley in 2007, and BS in Electrical and Computer Engineering from Cornell University in 2005.