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 2018):


Ph.D.University of California at San Diego2003

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

Recent Publications

  1. Hager, C; Pfister, HD, Approaching Miscorrection-Free Performance of Product Codes With Anchor Decoding, Ieee Transactions on Communications, vol. 66 no. 7 (July, 2018), pp. 2797-2808 [doi]
  2. Hager, C; Pfister, HD, Nonlinear interference mitigation via deep neural networks, 2018 Optical Fiber Communications Conference and Exposition, Ofc 2018 Proceedings (June, 2018), pp. 1-3, ISBN 9781943580385  [abs]
  3. H├Ąger, C; Pfister, HD, Nonlinear interference mitigation via deep neural networks, Optics Infobase Conference Papers, vol. Part F84-OFC 2018 (January, 2018) [doi]  [abs]
  4. Rengaswamy, N; Calderbank, AR; Kadhe, S; Pfister, HD, Synthesis of Logical Clifford Operators via Symplectic Geometry., Corr, vol. abs/1803.06987 (2018)
  5. Charbonneau, P; Li, YC; Pfister, HD; Yaida, S, Cycle-expansion method for the Lyapunov exponent, susceptibility, and higher moments., Physical Review. E, vol. 96 no. 3-1 (September, 2017), pp. 032129 [doi]  [abs]
Recent Grant Support