Henry Pfister

Henry Pfister
  • Professor in the Department of Electrical and Computer Engineering
  • Associate Professor of Mathematics (Secondary)
External address: 140 Science Dr., 305 Gross Hall, Durham, NC 27708
Internal office address: 90984, 315 Gross Hall, Durham, NC 27708
Phone: (919) 660-5288

Henry D. Pfister received his Ph.D. in Electrical Engineering in 2003 from the University of California, San Diego and is currently a professor in the Electrical and Computer Engineering Department of Duke University with a secondary appointment in Mathematics.  Prior to that, 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 and a coauthor of a 2016 Symposium on the Theory of Computing (STOC) best paper.  He has served the IEEE Information Theory Society as a member of the Board of Governors (2019-2022), an Associate Editor for the IEEE Transactions on Information Theory (2013-2016), and a Distinguished Lecturer (2015-2016).  He was also the General Chair of the 2016 North American School of Information Theory.

Education & Training
  • Ph.D., University of California, San Diego 2003

Selected Grants

FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis awarded by National Science Foundation (Principal Investigator). 2019 to 2022

CIF: Small: Improving Quantum Computing and Classical Communication using Discrete Sets of Unitary Matrices awarded by National Science Foundation (Co Investigator). 2019 to 2022

HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation (Senior Investigator). 2019 to 2022

CIF: Small: Capacity via Symmetry awarded by National Science Foundation (Principal Investigator). 2017 to 2021

Collaborative Research: Advanced Coding Techniques for Next-Generation Optical Communications awarded by National Science Foundation (Principal Investigator). 2016 to 2019

CIF:Small: Design and Analysis of Spatially-Coupled Coding Systems awarded by Texas A&M University (Principal Investigator). 2015 to 2017

Request for Support for U.S. Participants of the 2015 Workshop on Sensing and Analysis of High-Demensional Data awarded by National Science Foundation (Principal Investigator). 2015 to 2016

Oliari, V., et al. “Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration.” Journal of Lightwave Technology, vol. 38, no. 12, June 2020, pp. 3114–24. Scopus, doi:10.1109/JLT.2020.2994220. Full Text

Hager, C., et al. “Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation.” 2020 Optical Fiber Communications Conference and Exhibition, Ofc 2020  Proceedings, Mar. 2020.

Rengaswamy, Narayanan, et al. “Classical Coding Problem from Transversal $T$ Gates.Corr, vol. abs/2001.04887, 2020.

Rengaswamy, Narayanan, et al. “Quantum-Message-Passing Receiver for Quantum-Enhanced Classical Communications.Corr, vol. abs/2003.04356, 2020.

Carpi, F., et al. “Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding.” 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019, Sept. 2019, pp. 922–29. Scopus, doi:10.1109/ALLERTON.2019.8919799. Full Text

Pfister, Henry D., and Rudiger L. Urbanke. “Near-Optimal Finite-Length Scaling for Polar Codes Over Large Alphabets.” Ieee Transactions on Information Theory, vol. 65, no. 9, Institute of Electrical and Electronics Engineers (IEEE), Sept. 2019, pp. 5643–55. Crossref, doi:10.1109/tit.2019.2915595. Full Text

Rengaswamy, N., et al. “Unifying the Clifford hierarchy via symmetric matrices over rings.” Physical Review A, vol. 100, no. 2, Aug. 2019. Scopus, doi:10.1103/PhysRevA.100.022304. Full Text

Lian, M., et al. “Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation.” Ieee International Symposium on Information Theory  Proceedings, vol. 2019-July, July 2019, pp. 161–65. Scopus, doi:10.1109/ISIT.2019.8849419. Full Text

Can, T., et al. “Kerdock Codes Determine Unitary 2-Designs.” Ieee International Symposium on Information Theory  Proceedings, vol. 2019-July, July 2019, pp. 2908–12. Scopus, doi:10.1109/ISIT.2019.8849504. Full Text

Tal, I., et al. “Polar Codes for the Deletion Channel: Weak and Strong Polarization.” Ieee International Symposium on Information Theory  Proceedings, vol. 2019-July, July 2019, pp. 1362–66. Scopus, doi:10.1109/ISIT.2019.8849705. Full Text

Pages

Reeves, G., et al. “Mutual Information as a Function of Matrix SNR for Linear Gaussian Channels.” Ieee International Symposium on Information Theory  Proceedings, vol. 2018-June, 2018, pp. 1754–58. Scopus, doi:10.1109/ISIT.2018.8437326. Full Text

Hager, C., and H. D. Pfister. “Nonlinear interference mitigation via deep neural networks.” 2018 Optical Fiber Communications Conference and Exposition, Ofc 2018  Proceedings, 2018, pp. 1–3.

Sabag, O., et al. “A single-letter upper bound on the feedback capacity of unifilar finite-state channels.” Ieee Transactions on Information Theory, vol. 63, no. 3, 2017, pp. 1392–409. Scopus, doi:10.1109/TIT.2016.2636851. Full Text

Sabag, O., et al. “Single-letter bounds on the feedback capacity of unifilar finite-state channels.” 2016 Ieee International Conference on the Science of Electrical Engineering, Icsee 2016, 2017. Scopus, doi:10.1109/ICSEE.2016.7806200. Full Text

Kumar, S., et al. “Beyond double transitivity: Capacity-achieving cyclic codes on erasure channels.” 2016 Ieee Information Theory Workshop, Itw 2016, 2016, pp. 241–45. Scopus, doi:10.1109/ITW.2016.7606832. Full Text

Hager, C., et al. “Density evolution for deterministic generalized product codes with higher-order modulation.” International Symposium on Turbo Codes and Iterative Information Processing, Istc, vol. 2016-October, 2016, pp. 236–40. Scopus, doi:10.1109/ISTC.2016.7593112. Full Text

Sanatkar, M. R., and H. D. Pfister. “Increasing the rate of spatially-coupled codes via optimized irregular termination.” International Symposium on Turbo Codes and Iterative Information Processing, Istc, vol. 2016-October, 2016, pp. 31–35. Scopus, doi:10.1109/ISTC.2016.7593071. Full Text

Hager, C., et al. “Deterministic and ensemble-based spatially-coupled product codes.” Ieee International Symposium on Information Theory  Proceedings, vol. 2016-August, 2016, pp. 2114–18. Scopus, doi:10.1109/ISIT.2016.7541672. Full Text

Kumar, S., et al. “Reed-muller codes achieve capacity on the quantum erasure channel.” Ieee International Symposium on Information Theory  Proceedings, vol. 2016-August, 2016, pp. 1750–54. Scopus, doi:10.1109/ISIT.2016.7541599. Full Text

Kudekar, S., et al. “Comparing the bit-MAP and block-MAP decoding thresholds of reed-muller codes on BMS channels.” Ieee International Symposium on Information Theory  Proceedings, vol. 2016-August, 2016, pp. 1755–59. Scopus, doi:10.1109/ISIT.2016.7541600. Full Text

Pages