- 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 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.
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.
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
Rengaswamy, N, Calderbank, R, Pfister, HD, and Kadhe, S. "Synthesis of Logical Clifford Operators via Symplectic Geometry." Ieee International Symposium on Information Theory Proceedings 2018-June (August 15, 2018): 791-795. Full Text
Hager, C., and H. D. Pfister. “Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications.” Ieee International Symposium on Information Theory Proceedings, vol. 2018-June, Aug. 2018, pp. 1590–94. Scopus, doi:10.1109/ISIT.2018.8437734. Full Text
Santi, E., et al. “Decoding Reed-Muller Codes Using Minimum- Weight Parity Checks.” Ieee International Symposium on Information Theory Proceedings, vol. 2018-June, Aug. 2018, pp. 1296–300. Scopus, doi:10.1109/ISIT.2018.8437637. Full Text
Hager, C., and H. D. Pfister. “Approaching Miscorrection-Free Performance of Product Codes with Anchor Decoding.” Ieee Transactions on Communications, vol. 66, no. 7, July 2018, pp. 2797–808. Scopus, doi:10.1109/TCOMM.2018.2816073. Full Text
Luo, Yi, and Henry Pfister. “Adversarial Defense of Image Classification Using a Variational Auto-Encoder..” Corr, vol. abs/1812.02891, 2018.
Häger, C., and H. D. Pfister. “Nonlinear interference mitigation via deep neural networks.” Optics Infobase Conference Papers, vol. Part F84-OFC 2018, Jan. 2018. Scopus, doi:10.1364/OFC.2018.W3A.4. Full Text
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.
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.
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