Associate Professor in the Department of Electrical and Computer Engineering
Galen Reeves joined the faculty at Duke University in Fall 2013, and is currently an Associate 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, and he was a postdoctoral associate in the Departments of Statistics at Stanford University from 2011 to 2013. His research interests include information theory and high-dimensional statistics. He received the NSF CAREER award in 2017.
Appointments and Affiliations
- Associate Professor in the Department of Electrical and Computer Engineering
- Associate Professor of Statistical Science
- Office Location: 140 Science Dr., 321 Gross Hall, Durham, NC 27708
- Office Phone: (919) 668-4042
- Email Address: firstname.lastname@example.org
- Ph.D. University of California - Berkeley, 2011
Information theory, high-dimensional statistical inference, statistical signal processing, compressed sensing, machine learning
- ECE 587: Information Theory
- ECE 741: Compressed Sensing and Related Topics
- MATH 228L: Probability for Statistical Inference, Modeling, and Data Analysis
- STA 240L: Probability for Statistical Inference, Modeling, and Data Analysis
- STA 493: Research Independent Study
- STA 563: Information Theory
- STA 693: Research Independent Study
- STA 711: Probability and Measure Theory
- STA 741: Compressed Sensing and Related Topics
- STA 891: Topics for Preliminary Exam Preparation in Statistical Science
In the News
- Meet the Newly Tenured Faculty of 2021 (Sep 21, 2021 | Office of Faculty Advancement)
- Modeling Traffic with Self-Driving Cars (Mar 2, 2017 | Pratt School of Engineering)
- Reeves, G., and H. D. Pfister. “Reed–Muller Codes on BMS Channels Achieve Vanishing Bit-Error Probability for All Rates Below Capacity.” IEEE Transactions on Information Theory, January 1, 2023. https://doi.org/10.1109/TIT.2023.3286452.
- Reeves, G., and H. D. Pfister. “Achieving Capacity on Non-Binary Channels with Generalized Reed-Muller Codes.” In IEEE International Symposium on Information Theory - Proceedings, 2023-June:2057–62, 2023. https://doi.org/10.1109/ISIT54713.2023.10206574.
- Van Den Boom, W., G. Reeves, and D. B. Dunson. “Erratum: Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation (Biometrika (2021) 108 (269-282) DOI: 10.1093/biomet/asaa068).” Biometrika 109, no. 1 (March 1, 2022): 275. https://doi.org/10.1093/biomet/asab019.
- Behne, J. K., and G. Reeves. “Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity.” In Proceedings of Machine Learning Research, 151:8650–72, 2022.
- Goldfeld, Z., K. Greenewald, T. Nuradha, and G. Reeves. “k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension.” In Advances in Neural Information Processing Systems, Vol. 35, 2022.