Henry Pfister, Associate Professor of Electrical and Computer Engineering and Mathematics

Henry Pfister

Henry D. Pfister received his Ph.D. in electrical engineering in 2003 from the University of California, San Diego and is currently an associate professor in the Electrical and Computer Engineering Department of Duke University with a secondary appointment in Mathematics.  Prior to that, he was a professor at Texas A&M University (2006-2014), a post-doctoral fellow at the École Polytechnique Fédérale de Lausanne (2005-2006), and a senior engineer at Qualcomm Corporate R&D in San Diego (2003-2004).

He received the NSF Career Award in 2008 and a Texas A&M ECE Department Outstanding Professor Award in 2010.  He is a coauthor of the 2007 IEEE COMSOC best paper in Signal Processing and Coding for Data Storage and a coauthor of a 2016 Symposium on the Theory of Computing (STOC) best paper.  He served as an Associate Editor for the IEEE Transactions on Information Theory (2013-2016) and a Distinguished Lecturer of the IEEE Information Theory Society (2015-2016).

His current research interests include information theory, communications, probabilistic graphical models, machine learning, and deep neural networks.

Office Location:  140 Science Dr., 305 Gross Hall, Durham, NC 27708
Office Phone:  (919) 660-5288
Email Address: send me a message

Teaching (Fall 2019):


Ph.D.University of California at San Diego2003

Error-correcting codes (Information theory) • Information Theory • Signal processing • Wireless communication systems

Recent Publications

  1. 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 (April, 2019), pp. 2252-2283 [doi]  [abs]
  2. Schmidt, C; Pfister, HD; Zdeborová, L, Minimal sets to destroy the k-core in random networks., Physical Review. E, vol. 99 no. 2-1 (February, 2019), pp. 022310 [doi]  [abs]
  3. Yoo, I; Imani, MF; Sleasman, T; Pfister, HD; Smith, DR, Enhancing Capacity of Spatial Multiplexing Systems Using Reconfigurable Cavity-Backed Metasurface Antennas in Clustered MIMO Channels, Ieee Transactions on Communications, vol. 67 no. 2 (February, 2019), pp. 1070-1084, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  4. Sheikh, A; GraellAmat, A; Liva, G; Häger, C; Pfister, HD, On Low-Complexity Decoding of Product Codes for High-Throughput Fiber-Optic Systems, International Symposium on Turbo Codes and Iterative Information Processing, Istc, vol. 2018-December (January, 2019) [doi]  [abs]
  5. Lian, M; Häger, C; Pfister, HD, What can machine learning teach us about communications?, 2018 Ieee Information Theory Workshop, Itw 2018 (January, 2019) [doi]  [abs]
Recent Grant Support