Prof. Feifei Gao, IEEE Fellow, Tsinghua University, China

Bio: Feifei Gao (Fellow, IEEE) received the B.Eng. degree from Xi’an Jiaotong University, Xi’an, China, in 2002, the M.Sc. degree from McMaster University, Hamilton, ON, Canada, in 2004, and the Ph.D. degree from the National University of Singapore, Singapore, in 2007. Since 2011, he has been with the Department of Automation, Tsinghua University, Beijing, China, where he is currently a tenured Full Professor. He has authored/co-authored more than 150 refereed IEEE journal articles and more than 150 IEEE conference proceeding papers that are cited more than 12400 times in Google Scholar. His research interests include signal processing for communications, array signal processing, convex optimizations, and artificial intelligence-assisted communications. He also served as the Symposium Co-Chair for the 2019 IEEE Conference on Communications (ICC), the 2018 IEEE Vehicular Technology Conference Spring (VTC), the 2015 IEEE Conference on Communications (ICC), the 2014 IEEE Global Communications Conference (GLOBECOM), and the 2014 IEEE Vehicular Technology Conference Fall (VTC), and a technical committee member for more than 50 IEEE conferences. He served as an Editor for IEEE Transactions on Wireless Communications, the Lead Guest Editor for the IEEE Journal of Selected Topics in Signal Processing, and a Senior Editor for IEEE Transactions on Cognitive Communications and Networking, IEEE Signal Processing Letters, IEEE Communications Letters, IEEE Wireless Communications Letters, and China Communications.

Speech Title: Key Technologies and Prototype Design for Integrated Sensing and Communications System

Abstract: In the future, millions of base stations (BSs) and billions of users (UEs) will natively build an integrated sensing and communications (ISAC) system, which can utilize intelligent ubiquitous methods to realize the ultimate goal of sensing, i.e., constructing the global mapping from real physical world to digital twin world, while providing communications services at the same time. For this purpose, we conduct a series of theoretical and technical researches on ISAC, in which we decompose the real physical world into static environment, dynamic targets, and various object materials. The ubiquitous static environment occupies the vast majority of the physical world, for which we design static environment reconstruction (SER) scheme to obtain the layout and point cloud information of static buildings. The dynamic targets floating in static environments create the spatiotemporal transition of the physical world, for which we design comprehensive dynamic target sensing (DTS) scheme to detect, estimate, track, image and recognize the dynamic targets in real-time. The object materials enrich the electromagnetic laws of the physical world, for which we develop object material recognition (OMR) scheme to estimate the electromagnetic coefficient of the objects. Finally, based on these theoretical researches, we build an ISAC hardware prototype platform working in millimeter wave frequency band, realizing high-precision SER, DTS, and basic OMR, which provides preliminary verification for building the digital twin for communications networks.

 

Prof. Yudong Zhang , IET/EAI/Fellow, IEEE/ACM Senior Member, Southeast University, China

Bio: Yudong Zhang, chief professor at Southeast University, is a national high-level talent. His research interests include artificial intelligence, deep learning, and medical image processing. He is a fellow of IET/ EAI/ BCS, a senior member of IEEE and ACM, and an ACM distinguished speaker. He was recognized as a highly cited researcher by Clarivate Analytics from 2019 to 2024 and was among the top 2% of scientists in the world according to Stanford University from 2020 to 2023. He received the Emerald Citation of Excellence award in 2017 and the Best Paper Award at the Information Fusion in 2022, among others. Three of his papers were included in the UK's Research Excellence Framework 2021.

Speech Title: Colorectal Cancer Research: Challenges, Fundamental Progress, and Transdisciplinary Innovation

Abstract: Colorectal cancer (CRC) remains a pressing global healthcare challenge, demanding systematic advancements in its management paradigm. This report provides a high-level overview of the evolving landscape of CRC research and clinical practice, focusing on the transformative role of integrated innovation in diagnosis, therapy, and translational medicine. It highlights the strategic value of technological breakthroughs, including advanced diagnostic approaches, computer-aided solutions, and novel therapeutic strategies, in advancing CRC care. Concluding with key future research priorities centered on validation, safety, and preclinical translational studies, this work underscores the synergy between basic research and clinical application, offering a strategic outlook for mitigating the global CRC burden.
 

Prof. Yoshinori Dobashi, Hokkaido University/Prometech CG Research, Japan

Bio: Yoshinori Dobashi is a Professor at Hokkaido University, Japan. His research interests center on computer graphics, including realistic image synthesis, ecient rendering, and sound modeling for virtual reality applications. He received his BE, ME and PhD in Engineering in 1992, 1994, and 1997, respectively, from Hiroshima University. He worked at Hiroshima City University from 1997 to 2000 as a research associate.

Speech Title: Inverse Problems and Feedback Control for Expressive Image Synthesis

Abstract: This talk presents our research on inverse problems and feedback control in computer graphics, with a focus on modeling and controlling natural phenomena such as clouds and fluids. In traditional computer graphics, realistic images are generated through forward processes, where simulation and rendering are performed based on given parameters such as geometry, material properties, and lighting conditions. However, in many creative tasks, users often start with a desired visual outcome and seek a way to reproduce it. This leads to inverse problems, where the goal is to estimate parameters that produce the target results. In this talk, I will first introduce our work on the inverse design of clouds, where the visual characteristics of clouds, including their shape and color, are specified, and simulation parameters are automatically controlled to reproduce the desired results. I will also briefly discuss related work on editing fluid simulations.

 

Prof. Nannan Wang, Xidian University, China

Bio: Nannan Wang is a Professor and doctoral supervisor at Xidian University. He serves as the Associate Director of the State Key Laboratory of Integrated Services Networks. His recent research focuses on cross-domain image reconstruction and credible identity authentication, specifically including cross-domain image reconstruction (such as image translation, image synthesis and image restoration, etc.), object identification (such as face recognition, behavior recognition and person re-identification), and trustworthy machine learning (such as adversarial attacks and defenses with noisy samples and robust learning with noisy labels). He has over 200 publications in prominent international journals such as IEEE TPAMI, IJCV and conferences such as CVPR, ICCV, ICML, NeurIPS, etc. He has granted over 30 national invention patents, 7 of which have achieved patent technology transfer. He has three software copyright. His received several awards including the First Prize of Natural Science of Ministry of Education, the First Prize of Science and Technology of Shaanxi Province, the Second Prize of the Natural Science of Chinese Society of Image and Graphics, the Excellent Doctoral Dissertation of Chinese Association for Artificial Intelligence and the Excellent Doctoral Dissertation Award of Shaanxi Province, etc. He has led various research projects including the National Science Fund for Excellent Young Scholars of China, the Joint Funds Key Program, the General Program, the Youth Scientists Fund of the National Natural Science Foundation of China, as well as the sub-projects under the National Key Research and Development Program of China and the Joint Funds of Ministry of Education of China, etc. He served as Associate-Editor-in-Chief of the international journal "Visual Computer" and editor board member of “Neural Network".

Speech Title: Efficient Visual Content Generation

Abstract: To address the issues of high energy consumption and carbon emissions caused by the high computational complexity of visual content generation models, this report investigates neural network compression and stable training methods for image generation models from three perspectives: architectural design, inference steps, and parameter quantization, with the goal of enabling green, low‑carbon model lightweighting technologies. Specifically, the work includes: (1) Optimizing network architectures to reduce the number of model parameters or the amount of parameter activations; (2) Designing single-step algorithms for the inference phase of generative models to reduce inference latency; (3) Applying low-bit quantization to models to further shrink model size while maintaining stable training. These three lines of research are mutually reinforcing and are expected to form a comprehensive lightweighting solution for large models targeting edge-side deployment needs, achieving an excellent balance between resource consumption and model performance.