Galen Reeves

Associate Professor in the Department of Electrical and Computer Engineering

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: (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

  • ECE 587: Information Theory
  • ECE 899: Special Readings in Electrical Engineering
  • MATH 228L: Probability for Statistical Inference, Modeling, and Data Analysis
  • STA 240L: Probability for Statistical Inference, Modeling, and Data Analysis
  • STA 563: Information Theory
  • STA 693: Research Independent Study
  • STA 711: Probability and Measure Theory

In the News

Representative Publications

  • 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.
  • Kipnis, A., and G. Reeves. “Gaussian Approximation of Quantization Error for Estimation from Compressed Data.” Ieee Transactions on Information Theory 67, no. 8 (August 1, 2021): 5562–79. https://doi.org/10.1109/TIT.2021.3083271.
  • VAN DEN Boom, W., G. Reeves, and D. B. Dunson. “Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation.” Biometrika 108, no. 2 (June 2021): 269–82. https://doi.org/10.1093/biomet/asaa068.
  • Reeves, Galen. “A Two-Moment Inequality with Applications to Rényi Entropy and Mutual Information.” Entropy (Basel, Switzerland) 22, no. 11 (November 2020): E1244. https://doi.org/10.3390/e22111244.
  • Barbier, J., and G. Reeves. “Information-theoretic limits of a multiview low-rank symmetric spiked matrix model.” In Ieee International Symposium on Information Theory  Proceedings, 2020-June:2771–76, 2020. https://doi.org/10.1109/ISIT44484.2020.9173970.