My research interests lie at the intersection of signal processing, statistics, and information theory, with applications in compressed sensing, robust statistics, massive data storage and retrieval, neuroscience, and machine learning. With the era of information glut upon us, many of the most important scientific and technological advances of the next several decades will follow from our ability to collect, understand, and communicate massive amounts of data. An overall theme in my research is to draw upon mathematical tools from a wide variety of disciplines -- such as random matrix theory, convex optimization, statistical decision theory, and statistical physics -- to understand the limits of what is possible (and what is impossible) in problems of high-dimensional statistical inference, and also to figure out how we can reach these limits using computationally practical methods. My Ph.D. dissertation used tools from information theory to provide a sharp characterization of the problem of sparsity pattern recovery in compressed sensing.
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.
Publications (Updated July 2016)
Courses at Duke
Courses at Stanford