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:
  • website

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

• 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)

Representative Publications

• Van Den Boom, W; Reeves, G; Dunson, DB, Erratum: Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation (Biometrika (2021) 108 (269-282) DOI: 10.1093/biomet/asaa068), Biometrika, vol 109 no. 1 (2022) [10.1093/biomet/asab019] [abs].
• Kipnis, A; Reeves, G, Gaussian Approximation of Quantization Error for Estimation from Compressed Data, Ieee Transactions on Information Theory, vol 67 no. 8 (2021), pp. 5562-5579 [10.1109/TIT.2021.3083271] [abs].
• Reeves, G, A Two-Moment Inequality with Applications to Rényi Entropy and Mutual Information., Entropy, vol 22 no. 11 (2020) [10.3390/e22111244] [abs].
• Barbier, J; Reeves, G, Information-theoretic limits of a multiview low-rank symmetric spiked matrix model, Ieee International Symposium on Information Theory Proceedings, vol 2020-June (2020), pp. 2771-2776 [10.1109/ISIT44484.2020.9173970] [abs].
• Mathews, H; Mayya, V; Volfovsky, A; Reeves, G, Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise, 2019 Ieee 8th International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2019 Proceedings (2019), pp. 699-703 [10.1109/CAMSAP45676.2019.9022612] [abs].