Academic Keynote Speakers

Dariush Divsalar received the Ph.D. degree in electrical engineering from UCLA, in 1978. Since then, he has been with NASA’s Jet Propulsion Laboratory (JPL), California Institute of Technology (Caltech), Pasadena, where he is a Fellow. At JPL, he has been involved with developing state-of-the-art technology for advanced deep-space communications systems and future NASA space exploration. He is an adjunct professor at the UCLA Department of Electrical and Computer Engineering. He has taught graduate courses in communications and coding at UCLA and Caltech since 1986. He has published more than 300 technical papers, coauthored a book, contributed to three other books, and holds 30 U.S. patents. Dr. Divsalar was a co-recipient of the 1986 paper award of the IEEE Transactions on Vehicular Technology. He was also a co-recipient of the joint paper award of the IEEE Information Theory and IEEE Communication Theory societies in 2008. The IEEE Communication Society has selected one of his papers for inclusion in a book entitled The Best of the Best: Fifty Years of Communications and Networking Research. He served as an Editor for the IEEE Transactions on Communications from 1989 to 1996. A fellow of IEEE since 1997. He has received over 50 NASA Tech Brief awards, a NASA Exceptional Engineering Achievement Medal in 1996, IEEE Alexander Graham Bell Medal in 2014, an Ellis Island Medal of Honor in 2023, and a NASA Distinguished Public Service Medal in 2023. He was elected to the National Academy of Engineering in 2024.

Abstract: Error-control coding schemes for fiber-optic communication systems are challenged by the high data rates that must be achieved, necessitating low-complexity, yet performant, solutions. This talk will review some of the approaches that are being proposed (and taken) to achieve beneficial trade-offs between code performance and decoding complexity, both in long-haul telecommunications applications and in shorter-length optical interconnects.

Bio: Frank is a professor of electrical and computer engineering at the University of Toronto, where he has been a faculty member since 1991 (the year he received his Ph.D., also from U of T). He has worked on a number of topics in coding, including the introduction of factor graphs, subspace codes for network coding, and staircase codes. He was general co-chair for the IEEE International Symposium on Information Theory held in Toronto in 2008. He has served on the Board of Governors of the IEEE Information Theory Society, including as Society President in 2010. He served as the Editor-in-Chief of the IEEE Transactions on Information Theory from 2014 to 2016. He has received a number of awards for research, teaching, and service including the 2010 IEEE ComSoc/ITSoc Joint Paper Award, the 2018 ITSoc Paper Award, the 2016 Aaron D. Wyner Distinguished Service Award, the 2019 Sustained Excellence in Teaching Award from the U of T Faculty of Applied Science and Engineering, and the 2023 IEEE Richard W. Hamming Medal.

Abstract: In this talk, we present a survey of recent progress in the development of random access protocols for large-scale wireless networks. We highlight the pivotal role of coding theory in the design of modern random access schemes, starting from sparse graph models that have been instrumental in optimizing the performance of Aloha-like protocols. The unsourced multiple access (UMAC) paradigm is then introduced, revealing new challenges that can be effectively addressed through a coding-theoretic lens.

Bio: Gianluigi Liva received his Ph.D. in Electrical Engineering in 2006 from the University of Bologna, Italy. From 2004 to 2005, he was a visiting scholar at the University of Arizona, Tucson. Since 2006, he has been with the Institute of Communications and Navigation at the German Aerospace Center (DLR), where he currently leads the Information Transmission Group. In 2014, he was appointed as a lecturer in channel coding at the Institute for Communications Engineering (LNT), Technical University of Munich (TUM). His main research interests include satellite communications, random access techniques, and error control coding. He has been active in the 3GPP, DVB-SH, DVB-RCS, and DVB-S2 standardization groups, as well as in the standardization of error-correcting codes for space communications within the CCSDS. He was the co-chair of the 2018 IEEE European School of Information Theory and the TPC co-chair of the 2023 International Symposium on Topics in Coding. Since 2020, he has served as an Associate Editor for Coding and Information Theory for the IEEE Transactions on Communications.

Abstract: Spurred by recent advances in quantum science and engineering, interest in quantum information and coding has been increasing at a rapid pace. Much of this interest is driven by the promise of quantum computing and its potential to solve some problems much faster than classical computers. But, there are many other interesting problems that combine error-correcting codes and quantum information. In this talk, we will discuss both coding for quantum computing and coding for classical quantum channels. The focus will be on interesting connections between these two problems and the construction of efficient decoding algorithms for both. A key role will be played by belief propagation both the well-known classical algorithm and the lesser known version with quantum messages.

Bio: 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.

Abstract: For more than three quarters of a century, coding theory and computer memory technology have shared a virtuous cycle of mutual progress. Novel physical mechanisms for storing information have inspired the development of new coding methods that, in turn, have enabled the realization of these memory paradigms in increasingly capable data storage systems. The recent renaissance of machine learning has added another dimension to this relationship. In this talk, we will survey evolving trends in the harmonious interplay of coding theory, memory, and AI. Examples include coding techniques optimized by machine learning that ensure the functional correctness of advanced AI models, analog error- correcting codes that enhance reliability of memory-based AI accelerators, neural-assisted decoding algorithms that improve the robustness of storage devices, and generative AI models that facilitate performance prediction and code optimization in complex memory systems.

Bio: Paul H. Siegel received the S.B. and Ph.D. degrees in Mathematics from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 1975 and 1979, respectively. He held a Chaim Weizmann Postdoctoral Fellowship with the Courant Institute, New York University, New York, NY, USA. He was with the IBM Research Division, San Jose, CA, USA, from 1980 to 1995. He joined the faculty at the University of California San Diego (UCSD), La Jolla, CA, USA, in 1995, where he is currently a Distinguished Professor of Electrical and Computer Engineering with the Jacobs School of Engineering. He is affiliated with the Center for Memory and Recording Research where he holds an Endowed Chair and served as Director from 2000 to 2011. His research interests include information theory, coding techniques, and machine learning, with applications to digital data storage and transmission. He is a Fellow of the IEEE and was the 2015 Padovani Lecturer of the IEEE Information Theory Society. He was elected to the National Academy of Engineering in 2008.