Henry Pfister
- Professor in the Department of Electrical and Computer Engineering
- Associate Professor of Mathematics (Secondary)
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.
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
CIF: Small: Collaborative Research: Design and Analysis of Novel Compressed Sensing Algorithms via Connections with Coding Theory awarded by National Science Foundation (Principal Investigator). 2014 to 2016
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
Rengaswamy, N., et al. “Classical Coding Problem from Transversal T Gates.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, June 2020, pp. 1891–96. Scopus, doi:10.1109/ISIT44484.2020.9174408. Full Text
Brandsen, S., et al. “Adaptive Procedures for Discriminating Between Arbitrary Tensor-Product Quantum States.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, June 2020, pp. 1933–38. Scopus, doi:10.1109/ISIT44484.2020.9174234. Full Text
Buchberger, A., et al. “Pruning Neural Belief Propagation Decoders.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, June 2020, pp. 338–42. Scopus, doi:10.1109/ISIT44484.2020.9174097. Full Text
Coskun, M. C., et al. “Successive Cancellation Inactivation Decoding for Modified Reed-Muller and eBCH Codes.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, June 2020, pp. 437–42. Scopus, doi:10.1109/ISIT44484.2020.9174226. 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. “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
Pages
Rengaswamy, N., et al. “Quantum Advantage via Qubit Belief Propagation.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, 2020, pp. 1824–29. Scopus, doi:10.1109/ISIT44484.2020.9174494. Full Text
Lian, M., et al. “Decoding Reed-Muller Codes Using Redundant Code Constraints.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, 2020, pp. 42–47. Scopus, doi:10.1109/ISIT44484.2020.9174087. Full Text
Brandsen, S., et al. “Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, 2020, pp. 1897–902. Scopus, doi:10.1109/ISIT44484.2020.9174150. Full Text
Thangaraj, A., and H. D. Pfister. “Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes.” Ieee International Symposium on Information Theory Proceedings, vol. 2020-June, 2020, pp. 263–68. Scopus, doi:10.1109/ISIT44484.2020.9174065. Full Text
Häger, C., et al. “Model-based machine learning for joint digital backpropagation and PMD compensation.” Optics Infobase Conference Papers, vol. Part F174-OFC 2020, 2020. Scopus, doi:10.1364/OFC-2020-W3D.3. Full Text
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
Pages
Häger, C., et al. Density evolution for deterministic generalized product codes on the binary erasure channel at high rates. 1 July 2017. Scopus, doi:10.1109/TIT.2017.2689783. Full Text
Kumar, Santhosh, et al. A Proof of Threshold Saturation for Spatially-Coupled LDPC Codes on BMS Channels. 2013.
Nguyen, Phong S., et al. Spatially-Coupled Codes and Threshold Saturation on Intersymbol-Interference Channels. 2011.
Kim, Byung-Hak, et al. Message-Passing Inference on a Factor Graph for Collaborative Filtering. 2010.
Kim, Byung-Hak, and Henry D. Pfister. An Iterative Joint Linear-Programming Decoding of LDPC Codes and Finite-State Channels. 2010.
Nguyen, Phong S., et al. On Multiple Decoding Attempts for Reed-Solomon Codes. 2010.
Zhang, Fan, and Henry D. Pfister. On the Iterative Decoding of High-Rate LDPC Codes With Applications in Compressed Sensing. 2009.
Pfister, Henry D., and Igal Sason. Capacity-Achieving Ensembles of Accumulate-Repeat-Accumulate Codes for the Erasure Channel with Bounded Complexity. 2005.
Pfister, Henry D., et al. Bounds on the decoding complexity of punctured codes on graphs. 2004.
Pfister, Henry D. The capacity of finite-state channels in the high-noise regime. Cambridge University Press. Crossref, doi:10.1017/cbo9780511819407.007. Full Text