Galen Reeves

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:

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 586: Vector Space Methods with Applications
  • ECE 587: Information Theory
  • ECE 741: Compressed Sensing and Related Topics
  • ECE 899: Special Readings in Electrical Engineering
  • STA 563: Information Theory
  • STA 741: Compressed Sensing and Related Topics
  • STA 993: Independent Study

In the News

Representative Publications

  • Mayya, V; Reeves, G, Mutual Information in Community Detection with Covariate Information and Correlated Networks, 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019 (2019), pp. 602-607 [10.1109/ALLERTON.2019.8919733] [abs].
  • Reeves, G; Mayya, V; Volfovsky, A, The Geometry of Community Detection via the MMSE Matrix, Ieee International Symposium on Information Theory Proceedings, vol 2019-July (2019), pp. 400-404 [10.1109/ISIT.2019.8849594] [abs].
  • Kipnis, A; Reeves, G, Gaussian Approximation of Quantization Error for Estimation from Compressed Data, Ieee International Symposium on Information Theory Proceedings, vol 2019-July (2019), pp. 2029-2033 [10.1109/ISIT.2019.8849826] [abs].
  • Reeves, G; Pfister, HD, The Replica-Symmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact, Ieee Transactions on Information Theory, vol 65 no. 4 (2019), pp. 2252-2283 [10.1109/TIT.2019.2891664] [abs].
  • Bertran, M; Martinez, N; Papadaki, A; Qiu, Q; Rodrigues, M; Reeves, G; Sapiro, G, Adversarially learned representations for information obfuscation and inference, 36th International Conference on Machine Learning, Icml 2019, vol 2019-June (2019), pp. 960-974 [abs].