| Prof. Yue Gao, IEEE FellowFudan University, ChinaYue Gao is a Chair Professor and Dean of the Institute of Space Internet at Fudan University, China. He is a Fellow of the IEEE, the IET and CIC. He received his MSc and PhD from the Queen Mary University of London (QMUL) U.K. in 2003 and 2007. He has worked as a lecturer, senior lecturer, reader, and chair professor at QMUL and the University of Surrey. His research interests include satellite internet and space-air-ground integrated networks. He was a co-recipient of the EU Horizon Prize Award on Collaborative Spectrum Sharing and elected an Engineering and Physical Sciences Research Council Fellow. He is a member of the Board of Governors and Distinguished Lecturer of the IEEE Vehicular Technology Society (VTS), Chair of the IEEE ComSoc Wireless Communication Technical Committee, and past Chair of the IEEE ComSoc Technical Committee on Cognitive Networks. Title:Plotinus: A Satellite Internet Digital Twin System Abstract: Developing space-air-ground integrated networks (SAGIN) demands sophisticated satellite Internet emulation tools that handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms often struggle with challenges such as detailed implementation across all network layers, real-time response, and scalability. In response to these challenges, this talk will introduce a digital twin system based on microservices for satellite Internet emulation named Plotinus. Plotinus allows for real-time emulation with live network traffic, enhancing practical network models. This talk will introduce Plotinus’s effective emulation of dynamic satellite networks as a cross-layer, real-time, and scalable digital twin system. Moreover, this talk will introduce a handover scheme for mobile satellite networks to reduce handover latency compared to 3GPP Non-terrestrial Networks (NTN) and existing handover schemes. |
Prof. Carlo Vittorio CannistraciTsinghua University, ChinaDr. Cannistraci is a theoretical engineer and computational innovator. He is a Professor in the Tsinghua Laboratory of Brain and Intelligence (THBI) and an adjunct professor in the Department of Computer Science and in the Department of Biomedical Engineering at Tsinghua University. He directs the Center for Complex Network Intelligence (CCNI) in THBI, which seeks to create pioneering algorithms at the interface between information science, physics of complex systems, complex networks and machine intelligence, with a particular focus in brain/life-inspired computing for big data analysis. These computational methods are often applied to precision biomedicine, neuroscience, social and economic science. |
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| Prof. Meixia Tao, IEEE FellowShanghai Jiao Tong University, ChinaMeixia Tao is a Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, China. Her current research interests include wireless edge learning, coded caching, semantic communications, and multi-antenna technologies. She has published over 120 journal papers and 140 conference papers. She has received the First Prize in Natural Science from the Shanghai Municipality, the IEEE Marconi Prize Paper Award, the IEEE Heinrich Hertz Award for Best Communications Letters, and a number of Best Paper awards at interference conferences such as IEEE/CIC ICCC 2015 and WCSP 2022. Dr. Tao is currently an Associate Editor of the IEEE Transactions on Information Theory and the Vice-Chair of the Information Theory Society of the Chinese Institute of Electronics. She is a Fellow of IEEE and receives the National Science Fund for Distinguished Young Scholars. Title: Learning-based Coding and Transmission for Semantic Communications Abstract: As a new communication paradigm beyond Shannon, semantic communication (SemCom) can significantly reduce the required communication bandwidth and enhance downstream task performance by extracting and transmitting information directly relevant to the receiver's tasks. SemCom is envisioned to have transformative potential in 6G, facilitating a wide range of intelligent services, such as metaverse, smart surveillance, intelligent transportation, and robotic collaboration. In this talk, I will first provide an overview of the deep-learning-enabled SemCom frameworks as well as the main design challenges. Then I will introduce our latest research progress on novel designs of coding and transmission for SemCom towards practical implementation. These include: transmit data preprocessing via domain adaptation, receiver channel denoising via diffusion models, joint coding and modulation for digital semantic transmission, and codebook-assisted semantic coding. |
Prof. Shugong Xu, IEEE FellowShanghai University, ChinaShugong Xu is a professor at Shanghai University. After his graduation from Wuhan University in 1990, he received his Master degree in Pattern Recognition and Intelligent Control from Huazhong University of Science and Technology (HUST) in 1993, and Ph.D. degree in Communication and Electronic Systems from HUST in 1996. In his 25+ years career in research (over 15 years in top industrial research labs), he had over 80 issued US/WO/CN patents and published more than 180 peer-reviewed research papers. He was awarded "National Innovation Leadership Talent" from China government in 2013, IEEE Fellow in 2015. Shugong won the Award for Advances in Communication from IEEE Communication Society in 2017, and the First Prize in Shanghai"s Natural Science Award in 2023. His current research interests include 5G+/6G wireless communication systems, integrated sensing and communication, intelligent machine and pattern recognition, etc. Title: 6G ISAC and V2X Abstract：In 6G vision, sensing capability has been considered one of the critical new features and capabilities, along with AI capability etc. In this talk, we will discuss the recent advances in this new SaaS and its impact to the design of the future RAN evolution etc.. We will talk as well about its potential connection to the Vehicle to everything (V2X ) intiative, along with the new challenges and oppurtunities in the related fields. |
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| Prof. Chengju LiEast China Normal University, ChinaChengju Li received the Ph.D. degree from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2014. From March 2015 to February 2016, he was a Post-Doctoral Researcher with the Department of Mathematics, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. From March 2016 to August 2016, he held a post-doctoral position with the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong. From July 2018 to September 2018, he visited the University of Paris VIII, Paris, France. He is currently a professor of Software Engineering Institute at East China Normal University, Shanghai, China. His research interests include exponential sums, coding theory, and cryptography. He serves as an Associate Editor for the AIMS journal Advances in Mathematics of Communications. Title：The dual codes of BCH codes over finite field Abstract：BCH codes are an interesting class of error-correcting codes in.coding theory and extensively studied in the literature. However, little is known about the dual codes of BCH codes.In this talk, we present an overview of the recent progress onthe dual codes of BCH codes over finite fields. Our work ofthis talk is mainly joint with Binkai Gong and Professor Cunsheng Ding. |
| Assoc. Prof. Anthony BellottiUniversity of Nottingham Ningbo, ChinaDr. Anthony Bellotti is Associate Professor in the Department of Computer Science at University of Nottingham Ningbo, China. He received his PhD in machine learning from Royal Holloway, University of London in 2006 and was a Research Fellow in the Credit Research Centre at the University of Edinburgh from 2007 to 2010. He was senior lecturer at Imperial College London until 2019 where he taught quantitative methods in retail finance. His main research area is machine learning, with particular interest in credit risk models, dynamic survival models and reliable machine learning. He has published extensively on these topics in international refereed journals with 18 published papers over 10 years. Title:Reliable Machine Learning with Conformal Prediction |
