Galen Reeves

Assistant Professor in the Department of Electrical and Computer Engineering

Appointments and Affiliations

• Assistant Professor in the Department of Electrical and Computer Engineering
• Assistant Professor of Statistical Science

Contact Information

• Office Location: 140 Science Dr., 321 Gross Hall, Durham, NC 27708
• Office Phone: (919) 668-4042
• Email Address: galen.reeves@duke.edu
• Websites:
  • website

Education

• Ph.D. University of California at Berkeley, 2011

Research Interests

Information theory; high-dimensional statistical inference; statistical signal processing; compressed sensing; machine learning.

Courses Taught

• ECE 280L9: Signals and Systems - Lab
• ECE 280L: Introduction to Signals and Systems
• ECE 392: Projects in Electrical and Computer Engineering
• ECE 493: Projects in Electrical and Computer Engineering
• ECE 494: Projects in Electrical and Computer Engineering
• ECE 587: Information Theory
• ECE 741: Compressed Sensing and Related Topics
• STA 563: Information Theory
• STA 741: Compressed Sensing and Related Topics

In the News

• Pratt Senior Creates Model to Predict How Self-Driving Cars Might Affect Traffic (Mar 3, 2017 | Pratt School of Engineering )

Representative Publications

• Reeves, G, Additivity of information in multilayer networks via additive Gaussian noise transforms, 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol 2018-January (2018), pp. 1064-1070 [10.1109/ALLERTON.2017.8262855] [abs].
• Kipnis, A; Reeves, G; Eldar, YC; Goldsmith, AJ, Compressed sensing under optimal quantization, Ieee International Symposium on Information Theory Proceedings (2017), pp. 2148-2152 [10.1109/ISIT.2017.8006909] [abs].
• Reeves, G, Two-moment inequalities for Rényi entropy and mutual information, Ieee International Symposium on Information Theory Proceedings (2017), pp. 664-668 [10.1109/ISIT.2017.8006611] [abs].
• Reeves, G, Conditional central limit theorems for Gaussian projections, Ieee International Symposium on Information Theory Proceedings (2017), pp. 3045-3049 [10.1109/ISIT.2017.8007089] [abs].
• Mainsah, BO; Reeves, G; Collins, LM; Throckmorton, CS, Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction., Journal of Neural Engineering, vol 14 no. 4 (2017) [10.1088/1741-2552/aa7525] [abs].