Henry Pfister, Addy Family Professor of Electrical and Computer Engineering

Henry D. Pfister received his Ph.D. in Electrical Engineering in 2003 from the University of California, San Diego and is currently the Addy Family Professor of Electrical and Computer Engineering at Duke University with a secondary appointment in Mathematics. Prior to joining Duke, he was an associate 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). His current research interests include information theory, error-correcting codes, quantum computing, and machine learning.

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, a coauthor of a 2016 Symposium on the Theory of Computing (STOC) best paper, and a corecipient of the Information Theory Society Paper Award in both 2021 and 2025. He has served the IEEE Information Theory Society as a member of the Board of Governors (2019-2023), an Associate Editor for the IEEE Transactions on Information Theory (2013-2016), and a Distinguished Lecturer (2015-2016). He was the General Chair of the 2016 North American School of Information Theory and a Technical Program Committee Co-Chair of the 2021 International Symposium on Information Theory.

Contact Info:

Office Location:	140 Science Dr., 305 Gross Hall, Durham, NC 27708
Office Phone:	(919) 660-5288
Email Address:
Web Page:	http://pfister.ee.duke.edu

Teaching (Spring 2026):

• ECE 590.17, ADVANCED TOPICS IN ECE Synopsis

  Gross Hall 318, MW 01:25 PM-02:40 PM

• ECE 586D.001, VECTOR SPACE METHODS APPL Synopsis

  Teer 203, MW 10:05 AM-11:20 AM

• ECE 586D.01D, VECTOR SPACE METHODS APPL Synopsis

  Teer 203, F 10:05 AM-11:20 AM

Education:

Ph.D.

University of California, San Diego

2003

Keywords:

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

Recent Publications   (More Publications)

1. Hirsch, R; Aharoni, Z; Pfister, HD; Permuter, HH, Neural Polar Decoders for Receivers in Wireless Communication, IEEE Transactions on Communications (January, 2026), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
2. Aharoni, Z; Huleihel, B; Pfister, HD; Permuter, HH, Optimized Polar Codes via Mutual Information Maximization With Neural Polar Decoders, IEEE Transactions on Communications, vol. 74 (January, 2026), pp. 5820-5832, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
3. Yao, H; Gökduman, M; Pfister, HD, Cluster Decomposition for Improved Erasure Decoding of Quantum LDPC Codes, IEEE Journal on Selected Areas in Information Theory, vol. 6 (January, 2025), pp. 176-188 [doi]  [abs]
4. Alinia, M; Mitchell, DGM; Yao, H; Pfister, HD, Decimation Strategies for Belief Propagation Decoding of Quantum LDPC Codes, 2025 13th International Symposium on Topics in Coding Istc 2025 (January, 2025) [doi]  [abs]
5. Aharoni, Z; Pfister, HD, Neural Polar Decoders for DNA Data Storage, IEEE Journal on Selected Areas in Information Theory, vol. 6 (January, 2025), pp. 403-416 [doi]  [abs]

Recent Grant Support

• Collaborative Research: NSF-BSF CIF: Small: Neural Estimation of Statistical Divergences: Theoretical Foundations and Applications to Communication Systems, National Science Foundation, 2023/07-2027/06.      
• QLCI - CI: Institute for Robust Quantum Simulation, University of Maryland, College Park, 2021/09-2026/08.      
• CIF: Medium: Coding Theory for DNA Storage: Synthesis, Retention, and Reconstruction, University of California - San Diego, 2022/08-2026/07.      
• Simplifying quantum characterization through physical symmetries, machine learning, and a top-down approach,, Army Research Office, 2020/10-2025/10.      
• Collaborative Research: CIF: Medium: QODED: Quantum codes Optimized for the Dynamics between Encoded Computation and Decoding using Classical Coding Techniques, National Science Foundation, 2021/10-2024/09.