Topic 1: Advanced Coding Technology
| Wai Ho MOW Associate Dean of Engineering (Undergraduate Studies), Professor The Hong Kong University of Science and Technology Title: On Short Linear Codes with Nearly Optimal Asymptotic Frame Error Rate Abstract: To be announced. |
| Xiaohu Tang Professor Southwest Jiaotong University Title: Research on Hardware-Friendly LDPC Code Abstract: To be announced. |
| Qin Huang Professor Beihang University Title: Decomposition and Combination: Way to Near-Optimal Decoding Abstract: To be announced. |
| Ling Liu Associate Professor Xidian University (Guangzhou) Title: Improving the Performance of Polar Codes using Feedback Abstract: To be announced. |
Topic 2: Semantic Communication
| Jun Chen Professor McMaster University Title: On the Fundamental Limits of Generative Communication Abstract: Motivated by the emerging paradigm of generative communication, this talk explores the problem of channel-aware optimal transport, where a block of i.i.d. random variables is transmitted through a memoryless channel to generate another block of i.i.d. random variables with a prescribed marginal distribution such that the end-to-end distortion is minimized. With unlimited common randomness available to the encoder and decoder, the source-channel separation architecture is shown to be asymptotically optimal as the blocklength approaches infinity. On the other hand, in the absence of common randomness, the source-channel separation architecture is generally suboptimal. For this scenario, a hybrid coding scheme is proposed, which partially retains the generative capabilities of the given channel while enabling reliable transmission of digital information. It is demonstrated that the proposed hybrid coding scheme can outperform both separation-based and uncoded schemes. Biography: Jun Chen received the B.S. degree in Electronic Engineering from Shanghai Jiao Tong University in 2001, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering from Cornell University in 2004 and 2006, respectively. He was a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign from September 2005 to July 2006, and a Postdoctoral Fellow at the IBM Thomas J. Watson Research Center from July 2006 to August 2007. Since September 2007 he has been with the Department of Electrical and Computer Engineering at McMaster University, where he is currently a Professor. He held the title of the Barber-Gennum Chair in Information Technology from 2008 to 2013 and the Joseph Ip Distinguished Engineering Fellow from 2016 to 2018. He was a recipient of the Josef Raviv Memorial Postdoctoral Fellowship (2006), the Early Researcher Award from the Province of Ontario (2010), the IBM Faculty Award (2010), the ICC Best Paper Award (2020), the JSPS Invitational Fellowship (2021), and the ECE Instructor Award (2023). He was an Associate Editor of the IEEE Transactions on Information Theory (2014 - 2016), an Editor of the IEEE Transactions on Green Communications and Networking (2020 - 2021), and a Guest Editor of the Special Issue on Modern Compression for the IEEE Journal on Selected Areas in Information Theory (2022). He is currently serving as an Associate Editor of the IEEE Transactions on Information Theory and an Associate Editor of the IEEE Transactions on Communications. |
| Kai Niu Professor Beijing University of Posts and Telecommunications Title: Semantic Information Theory and Method Abstract: The convergence of communication and artificial intelligence represents a pivotal trend in future information processing, with semantic information emerging as a new medium for information interaction. This talk begins by introducing the fundamental characteristics of semantic information, thati is synonymity, followed by a concise overview of the basic framework of semantic information theory, including its measurement system and the performance limits of semantic communication. Finally, it presents the system framework based on synonymous mapping and typical results of semantic encoding and transmission. It is foreseeable that semantic communication will become a new technological paradigm in future communications, offering promising application prospects Biography: Niu Kai received the B.S. degree in information engineering and the Ph.D. degree in signal and information processing from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 1998 and 2003, respectively. He is currently a Professor with the School of Artificial Intelligence, BUPT. His research interests include channel coding theory and applications, semantic communication and broadband wireless communication. He has published more than 200 academic papers, and his proposed high performance compilation code algorithm for polar code has become the mainstream scheme of 5G standard, and won the first prize of Natural Science of Science and Technology Award of the Chinese Institute of Electronics. |
| Yongjune Kim Associate Professor Pohang University of Science and Technology Title: CrossMPT: Cross-attention Message-passing Transformer for Error Correcting Codes Abstract: Error correcting codes (ECCs) are indispensable for reliable transmission in communication systems. Recent advancements in deep learning have catalyzed the exploration of ECC decoders based on neural networks. Among these, transformer-based neural decoders have achieved state-of-the-art decoding performance. We propose a novel Cross-Attention Message-Passing Transformer (CrossMPT), which shares key operational principles with conventional message-passing decoders. While conventional transformer-based decoders employ a self-attention mechanism without distinguishing between magnitude and syndrome embeddings, CrossMPT updates these two types of embeddings separately and iteratively via two masked cross-attention blocks. The mask matrices are determined by the code's parity-check matrix, which explicitly captures and removes irrelevant relationships between the magnitude and syndrome embeddings. Our experimental results show that CrossMPT significantly outperforms existing neural network-based decoders for various code classes. Notably, CrossMPT achieves this decoding performance improvement while significantly reducing memory usage, computational complexity, inference time, and training time. Biography: Yongjune Kim is an Associate Professor in the Department of Electrical Engineering at Pohang University of Science and Technology (POSTECH) and an Adjunct Professor at Yonsei University. His research interests include coding theory, communication theory, and machine learning. Before joining POSTECH, he was an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at DGIST and a Postdoctoral Research Associate in the Coordinated Science Laboratory (CSL) at the University of Illinois at Urbana-Champaign (UIUC), where he was mentored by Prof. Naresh R. Shanbhag and Prof. Lav R. Varshney. He also worked at Western Digital Research, Samsung Electronics, and Samsung Advanced Institute of Technology (SAIT). He received his PhD degree in Electrical and Computer Engineering (ECE) from Carnegie Mellon University (CMU), advised by Prof. B. V. K. Vijaya Kumar (Vijayakumar Bhagavatula). He earned his BS and MS degrees in ECE from Seoul National University (SNU), Korea, advised by Prof. Jong-Seon No. He received several awards, including the IEEE ComSoc Data Storage Technical Committee Best Student Paper Award, the Best Paper Award at the 2016 IEEE International Conference on Communications (ICC), the Best Paper Award (honorable mention) at the 2018 IEEE International Symposium on Circuits and Systems (ISCAS), and the Best Paper Award at the 31st Samsung Semiconductor Technology Symposium. He serves as an Editor for IEEE Transactions on Communications. |
| Bo Bai Director of Theory Lab, Chief Scientist of Information Theory Huawei Technologies Co., Ltd. Title: Forget BIT, It' s All about TOKEN!-Towards the Mathematical Theory of Semantic Abstract: To be announced. |
| Dong Liu Professor The University of Science and Technology of China Title: A Deep Learning Approach to the Rate-Distortion Bounds of Image Compression Abstract: Lossy image compression is a fundamental technology for visual communications. Classical image compression algorithms have reached a performance bottleneck. Recently, deep learning has enabled the development of end-to-end optimized image compression schemes, which have surpassed classical compression algorithms. This talk begins with a discussion of the ideal source coding system in information theory, followed by an outline of the practical constraints and assumptions involved in constructing practical end-to-end learned image compression schemes, along with their resulting performance gaps. We quantitatively analyze these gaps by estimating the bounds of the rate–distortion function of natural images. Moreover, by scaling up the parameter count of image compression models, we empirically validate the achievable rate–distortion performance and reveal the scaling law in lossy image compression. The talk concludes with a discussion on the connections between compression and intelligence, highlighting potential directions for future research in image compression. Biography: Dong Liu received the B.S. and Ph.D. degrees in electrical engineering from the University of Science and Technology of China (USTC), Hefei, China, in 2004 and 2009, respectively. He was a Member of Research Staff with Nokia Research Center, Beijing, China, from 2009 to 2012. He has been a faculty member at USTC since 2012 and currently holds the position of full professor. His research interests include image and video coding, processing, and visual intelligence. He has authored or co-authored more than 200 papers in international journals and conferences, which were cited more than 25,000 times according to Google Scholar (h-index is 60). He has more than 40 granted patents, and dozens of technique proposals adopted by standardization groups. He received 2009 IEEE TCSVT Best Paper Award and ISCAS 2025 Grand Challenge Top Creativity Paper Award. He and his students were winners of several technical challenges held in CVPR 2025, ICIP 2024, and ISCAS 2023. He is a Senior Member of IEEE, CCF, and CSIG, an elected member of IVMSP-TC of IEEE SP Society, an elected member of MSA-TC of IEEE CAS Society, and an elected member of Multimedia TC of CSIG. He serves or had served as the Chair of IEEE 1857.11 Standard Working Subgroup (also known as Future Video Coding Study Group), an Associate Editor for IEEE TIP, a Guest Editor for IEEE TCSVT, and a TPC co-chair for ICME 2026. |
| Tao Guo Associate Professor Southeast University Title: Rate-distortion Theory for Multi-user Semantic Compression Abstract: We consider the semantic rate-distortion problem motivated by task-oriented data compression with side information. The semantic information corresponding to the task, which is not observable to the encoder, shows impact on the observations through a joint probability distribution. The decoder is interested in recovering the observation and making an inference of the semantic information under certain distortion constraints. We establish the information-theoretic limits for the tradeoff between compression rates and distortions by fully characterizing the rate-distortion function. An estimation-compression (EC) separation scheme is also considered. Therein, the semantic information is intrinsic and not observable. The EC scheme first estimates the semantic information from the observed message and then compresses the estimation subject to a rate-distortion regime. The corresponding EC rate-distortion tradeoff is obtained. In particular, the EC separation scheme achieves the semantic rate-distortion function if the estimationis a sufficient statistic of the semantic information based on the observed message. Biography: Tao Guo is an Associate Professor at the School of Cyber Science and Engineering, Southeast University, Nanjing, China. He received his B.Eng. degree from Xidian University in 2013 and his Ph.D. degree from The Chinese University of Hong Kong in 2018. He has conducted research at the Technical University of Munich, Texas A&M University, University of California, Los Angeles, and Huawei Hong Kong Research Center. His research primarily focuses on information theory, information security, and privacy protection. He has published over 30 papers in information theory journals and conferences. |
Topic 3: Cryptography and Information Theory
| Amin Gohari Vice-Chancellor Associate Professor The Chinese University of Hong Kong Title: On the Source Model Key Agreement Problem Abstract: To be announced. Biography: Amin Gohari received his B.Sc. degree from Sharif University, Iran, in 2004 and his Ph.D. degree in electrical engineering from the University of California, Berkeley in 2010. Dr. Gohari received the 2010 Eli Jury Award from UC Berkeley, Department of Electrical Engineering and Computer Sciences, for “outstanding achievement in the area of communication networks,” and the 2009-2010 Bernard Friedman Memorial Prize in Applied Mathematics from UC Berkeley, Department of Mathematics, for “demonstrated ability to do research in applied mathematics.” He also received the Gold Medal from the 41st International Mathematical Olympiad (IMO 2000) and the First Prize from the 9th International Mathematical Competition for University Students (IMC 2002). He received the IEEE Iran Section Young Researcher Award in 2021. Dr. Gohari served as an Associate Editor for the IEEE Transactions on Information Theory from 2018-2021. He was also a finalist for the IEEE Jack Keil Wolf ISIT Student Paper Award for three consecutive years from 2008-2010 during his PhD. |
| Mitsugu Iwamoto Professor University of Electro-Communications Title: Information-theoretic Security, Revisited Abstract: To be announced. |
| Vinod M. Prabhakaran Associate Professor Tata Institute of Fundamental Research, India Title: Byzantine Distributed Function Computation Abstract: To be announced. |
Other Invited Talks
| Guodong Shi Associate Professor The University of Sydney Title: Differential Privacy over Affine Manifolds Abstract: We consider differential privacy mechanisms when the input data are constrained to lie in affine manifolds, which are available as prior knowledge to adversaries. In this setting, the definition of neighborhood adjacency must be formulated with respect to the intrinsic geometry of the manifolds. We demonstrate that such affine-manifold constraints can fundamentally alter the attainable privacy levels relative to the unconstrained case. In particular, we derive necessary and sufficient conditions under which differential privacy can be realized via structured noise injection mechanisms, wherein correlated Gaussian or Laplace noise distributions, rather than i.i.d. perturbations, are calibrated to the dataset. Based on these characterizations, we develop explicit noise calibration procedures that guarantee the tight realization of any prescribed privacy budget with a matching noise magnitude. We further show that the proposed framework admits direct applications to differentially private cloud-based control, privacy-preserving average consensus, and potentially to quantum information processing, all of which naturally involve affine-manifold constraints. Biography: Guodong Shi received the Ph.D. degree in systems theory from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China in 2010. From 2010 to 2014, he was a Postdoctoral Researcher at the ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden. From 2014 to 2018, he was with the Research School of Engineering, The Australian National University, Canberra, ACT, Australia, as a Lecturer and then Senior Lecturer, and a Future Engineering Research Leadership Fellow. Since 2019 he has been with the Australian Center for Robotics, The University of Sydney, NSW, Australia. His research interests include distributed control systems, quantum networking and decisions, social opinion dynamics, and their interface in renewable energies, robotics, and climate change. |
| Hsin-Po Wang Assistant Professor National Taiwan University Title: If Rand()%10 is Bad, How to Use TRNG Efficiently? Abstract: To be announced. Biograpgy: Hsin-Po Wang is an Assistant Professor in EE and GICE at National Taiwan University. His research interests lie in information theory and coding theory, where techniques in algebra, combinatorics, and probability theory are applied to polar codes, group testing, distributed storage, distributed computation, exact distribution shaping, and database optimization. Hsin-Po earned his B.Sc. in Math at NTU and completed his Ph.D. in Math at UIUC. He has held research positions at UC San Diego, UC Berkeley, and the Simons Institute for the Theory of Computing. Known for his extensive, creative use of Tikz figures in papers, Hsin-Po is equally passionate about speedrun techniques for educational purposes and recreational math such as assembling binder clips into fullerene-like structures. |
