Galen Reeves

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

Contact Information

  • Office Location: 140 Science Dr., 321 Gross Hall, Durham, NC 27708
  • Office Phone: +1 919 668 4042
  • Email Address: galen.reeves@duke.edu
  • Websites:

Education

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

Research Interests

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

Courses Taught

  • STA 891: Topics for Preliminary Exam Preparation in Statistical Science
  • STA 741: Compressed Sensing and Related Topics
  • STA 711: Probability and Measure Theory
  • STA 563: Information Theory
  • STA 493: Research Independent Study
  • ECE 741: Compressed Sensing and Related Topics
  • ECE 587: Information Theory

In the News

Representative Publications

  • 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 70, no. 2 (February 1, 2024): 920–49. 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.
  • 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.
  • 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.