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

Reeves, G., and H. D. Pfister. “The Replica-Symmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact.” Ieee Transactions on Information Theory, vol. 65, no. 4, Apr. 2019, pp. 2252–83. Scopus, doi:10.1109/TIT.2019.2891664. Full Text

Schmidt, Christian, et al. “Minimal sets to destroy the k-core in random networks.Physical Review. E, vol. 99, no. 2–1, Feb. 2019, p. 022310. Epmc, doi:10.1103/physreve.99.022310. Full Text

Yoo, I., et al. “Enhancing Capacity of Spatial Multiplexing Systems Using Reconfigurable Cavity-Backed Metasurface Antennas in Clustered MIMO Channels.” Ieee Transactions on Communications, vol. 67, no. 2, Feb. 2019, pp. 1070–84. Scopus, doi:10.1109/TCOMM.2018.2876899. Full Text

Sheikh, A., et al. “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, Jan. 2019. Scopus, doi:10.1109/ISTC.2018.8625279. Full Text

Lian, M., et al. “What can machine learning teach us about communications?2018 Ieee Information Theory Workshop, Itw 2018, Jan. 2019. Scopus, doi:10.1109/ITW.2018.8613331. Full Text

Häger, Christian, et al. “Revisiting Multi-Step Nonlinearity Compensation with Machine Learning.Corr, vol. abs/1904.09807, 2019.

Rengaswamy, Narayanan, et al. “Logical Clifford Synthesis for Stabilizer Codes.Corr, vol. abs/1907.00310, 2019.

Rengaswamy, Narayanan, et al. “On Optimality of CSS Codes for Transversal T.Corr, vol. abs/1910.09333, 2019.

Fougstedt, C., et al. “ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters.” European Conference on Optical Communication, Ecoc, vol. 2018-September, Nov. 2018. Scopus, doi:10.1109/ECOC.2018.8535430. Full Text

Häger, C., and H. D. Pfister. “Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning.” European Conference on Optical Communication, Ecoc, vol. 2018-September, Nov. 2018. Scopus, doi:10.1109/ECOC.2018.8535251. Full Text

Pages

Sabag, O., et al. “A single-letter upper bound on the feedback capacity of unifilar finite-state channels.” Ieee International Symposium on Information Theory  Proceedings, vol. 2016-August, 2016, pp. 310–14. Scopus, doi:10.1109/ISIT.2016.7541311. Full Text

Pfister, H. D., and R. Urbanke. “Near-optimal finite-length scaling for polar codes over large alphabets.” Ieee International Symposium on Information Theory  Proceedings, vol. 2016-August, 2016, pp. 215–19. Scopus, doi:10.1109/ISIT.2016.7541292. Full Text

Reeves, G., and H. D. Pfister. “The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact.” Ieee International Symposium on Information Theory  Proceedings, vol. 2016-August, 2016, pp. 665–69. Scopus, doi:10.1109/ISIT.2016.7541382. Full Text

Hager, C., et al. “Density evolution and error floor analysis for staircase and braided codes.” 2016 Optical Fiber Communications Conference and Exhibition, Ofc 2016, 2016. Scopus, doi:10.1364/ofc.2016.th2a.42. Full Text

Kudekar, S., et al. “Reed-Muller codes achieve capacity on erasure channels.” Proceedings of the Annual Acm Symposium on Theory of Computing, vol. 19-21-June-2016, 2016, pp. 658–69. Scopus, doi:10.1145/2897518.2897584. Full Text

Kumar, S., et al. “Spatially-coupled codes for write-once memories.” 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015, 2016, pp. 125–31. Scopus, doi:10.1109/ALLERTON.2015.7446994. Full Text

Lian, M., and H. D. Pfister. “Belief-propagation reconstruction for compressed sensing: Quantization vs. Gaussian approximation.” 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015, 2016, pp. 1106–13. Scopus, doi:10.1109/ALLERTON.2015.7447132. Full Text

Kudekar, Shrinivas, et al. “Reed-Muller codes achieve capacity on erasure channels.Stoc, edited by Daniel Wichs and Yishay Mansour, ACM, 2016, pp. 658–69.

Pfister, H. D., et al. “Symmetric product codes.” 2015 Information Theory and Applications Workshop, Ita 2015  Conference Proceedings, 2015, pp. 282–90. Scopus, doi:10.1109/ITA.2015.7309002. Full Text

Li, S., et al. “On the limits of treating interference as noise for two-user symmetric Gaussian interference channels.” Ieee International Symposium on Information Theory  Proceedings, vol. 2015-June, 2015, pp. 1711–15. Scopus, doi:10.1109/ISIT.2015.7282748. Full Text

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