Keynote Speakers

Xuemin (Sherman) Shen, University of Waterloo, Canada

Xuemin (Sherman) Shen is an University Professor at the University of Waterloo, Canada. His research interests include network resource management, wireless network security, AI for networks, and future communication systems. Professor Shen is the Technical Program Committee Chair for IEEE Globecom'24, Globecom'16, IEEE Infocom14, and IEEE VTC’10 Fall. He was the Editor-in-Chief of the IEEE Internet of Things Journal, IEEE Network, and IET Communications. Professor Shen served as the 2022-2023 President of IEEE Communications Society. He is a Fellow of the IEEE, Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada, a Foreign Member of Chinese Academy of Engineering, and an International Fellow of the Engineering Academy of Japan.

 

Title: High-Fidelity and Efficient Simulator for 6G Space-Terrestrial Integrated Networks
Abstract:
Space-terrestrial integrated networks are envisioned to realize a giant leap forward for the future sixth generation (6G) coverage expansion, bridging digital divide for remote areas and providing ubiquitous services. In this talk, we provide an overview of the team's recent endeavors in the high-fidelity and efficient simulator for 6G space-terrestrial integrated networks with low-Earth-orbit (LEO) mega-constellation satellites. Particularly, we introduce a systematic, scalable and comprehensive simulation architecture, which enables high-fidelity modeling of network configurations and high efficiency simulation of network operation and management capabilities, while providing users with intuitive visualizations. Our developed simulator can offer fresh perspectives and novel avenues for evaluating critical technologies and validating essential methods in the context of 6G space-terrestrial integrated networks and large-scale LEO constellation ultra-dense networking.

Qian Zhang , The Hong Kong University of Science and Technology, Hong Kong, China

Dr. Qian Zhang joined the Hong Kong University of Science and Technology (HKUST) in September 2005. She is currently the Head of the Division of Integrative Systems and Design (ISD), Tencent Professor of Engineering, and Chair Professor of the Department of Computer Science and Engineering (CSE) at HKUST. She is also the Director of the Microsoft Research Asia-HKUST Joint Laboratory, the Co-Director of the Huawei-HKUST Innovation Laboratory, and the Director of the HKUST Digital Life Research Center. Prior to this, she worked at Microsoft Research Asia from July 1999 as a research manager in the Wireless and Networking Group. Dr. Zhang has published more than 500 refereed papers in leading international journals and major conferences. She is the inventor of more than 50 international patents. Her current research interests include the Intelligent Internet of Things (AIoT), smart health, mobile computing and sensing, wireless networks, and network security.
Dr. Qian Zhang is an IEEE Fellow and a Fellow of the Hong Kong Academy of Engineering (HKAE). Dr. Zhang has won many awards, including the Ho Leung Ho Lee Foundation Science and Technology Innovation Award, the China Youth Science and Technology Award, the National Natural Science Second Prize (third winner), and the MIT TR100. Dr. Zhang served as the Editor-in-Chief of IEEE Transactions on Mobile Computing from 2020 to 2022. She is currently a member of the IEEE Infocom Steering Committee.

 

Title: Edge-AI Powered mmWave Sensing: Towards Intelligent Human-Centric Systems
Abstract:
The development of AI and the Internet of Things (IoTs) has brought new development opportunities to the industry. The advancement of intelligent sensing technology has laid the foundation for realizing human-centered intelligent scene. Many applications such as entertainment, fitness, and healthcare require accurate understanding of user behavior in a convenient and easy way. In this lecture, I will share some of our work on intelligent sensing technology based on mmWave signal. With single modality sensing, a dry eye disease screening solution will be introduced. With cross-modality knowledge transfer framework, detailed dry eye disease assessment as well as dynamic blood pressure waveform monitoring will be introduced. Finally, with multi-modal fusion idea, the hand tremors detection will be elaborated in this talk.

 

Zhiwen Yu, Harbin Engineering University, China

Dr. Zhiwen Yu is currently a vice president of Harbin Engineering University, China and a professor of Northwestern Polytechnical University, China. He has worked as an Alexander Von Humboldt Fellow at Mannheim University, Germany from Nov. 2009 to Oct. 2010, a research fellow at Kyoto University, Japan from Feb. 2007 to Jan. 2009, and a post-doctoral researcher at Nagoya University, Japan in 2006-2007. His research interests cover Ubiquitous Computing, Internet of Things, and Crowd Sensing and Computing. He is the Editor-in-Chief of CCF Transactions on Pervasive Computing and Interaction. He has served as an associate/guest editor for a number of international journals, such as IEEE Transactions on Human-Machine Systems, IEEE Communications Magazine, and ACM Transactions on Intelligent Systems and Technology. He received the CCF Young Scientist Award in 2011, the Humboldt Fellowship in 2008, and the CCF Excellent Doctoral Dissertation Award in 2006. He got the National Science Fund for Distinguished Young Scholars in 2017.

 

Title: Crowd Sensing 2.0: From Human-Centered to Heterogeneous Crowd Sensing

Abstract:

Crowd sensing is a new sensing paradigm that uses individual mobile and ubiquitous sensing capability to accomplish complex sensing tasks. In this speech, I will introduce our work in crowd sensing in various aspects, such as theory, methods, and platform. Furthermore, I will present the main idea of crowd sensing 2.0, including the featues and enabling technologies of heterogeneous crowd sensing.

 

Tony Q.S. Quek, Singapore University of Technology and Design, Singapore

Tony Q.S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, respectively. At Massachusetts Institute of Technology, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Associate Provost (AI & Digital Innovation) and Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD). He also serves as the Director of the Future Communications R&D Programme, and the ST Engineering Distinguished Professor. His current research topics include wireless communications and networking, AI-RAN, non-terrestrial networks, open radio access network, and 6G.

Dr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, the 2020 Nokia Visiting Professorship, the 2022 IEEE Signal Processing Society Best Paper Award, the 2024 IIT Bombay International Award For Excellence in Research in Engineering and Technology, the IEEE Communications Society WTC Recognition Award 2024, and the Public Administration Medal (Bronze). He is an IEEE Fellow, a WWRF Fellow, an AIAA Fellow, and a Fellow of the Academy of Engineering Singapore.

 

Title: From Theory to Practice in 6G AI-Native Network
Abstract:

With the advances in big data computing technology, AI already shows promising potentials in wireless industry, and we expect it will play an even more crucial role in 6G wireless networks. On the other hand, there is a future trend to explore the concurrent use of converged computer-and-communications infrastructure to run RAN and AI and Generative AI workloads, enhancing platform utilization and creating new monetization opportunities. Lastly, it is crucial to understand the radio interface requirements for running AI and Generative AI applications across consumer, enterprise, and government sectors. In this talk, we will first look at the theory aspect of an AI-native network through distributed learning and semantic communications. Next, we proceed to explore the practice aspect in implementing an Ai-native network through the concept of AI-RAN and explains how it can transform future networks with AI. In conclusion, we will also share some of our work through Singapore’s Future Communications Research and Development Programme (FCP).

David Gesbert, EURECOM, France

Prof. David Gesbert (Fellow, IEEE)  is serving as Director of EURECOM, Sophia Antipolis, France (www.eurecom.fr). He received the Ph.D. degree from TelecomParis, France, in 1997. From 1997 to 1999, he was with the Information Systems Laboratory, Stanford University. He was a founding engineer of Iospan Wireless Inc., a Stanford spin off pioneering MIMO-OFDM (currently Intel). Before joining EURECOM in 2004, he was with the Department of Informatics, University of Oslo. He has published about 350 articles and 25 patents, 7 of them winning  IEEE Best paper awards. He has been the Technical Program Co-Chair for ICC2017 and has been named a Thomson-Reuters Highly Cited Researchers in computer science.  He is a Board Member for the OpenAirInterface (OAI) Software Alliance. He was a previous awardee of an ERC Advanced Grant in the area of future networks. In 2020, he was also awarded funding by the French Interdisciplinary Institute on Artificial Intelligence for a Chair in the area of AI for the future IoT. In 2021, he received the Grand Prix in Research jointly from IMT and the French Academy of Sciences.

 

Title: Sensing and Charting in the Sky with UAV-aided 6G Networks

Abstract:

This talk explores the growing synergies between robotics, specifically flying robots, and next-generation wireless networks such as 6G. An overview of ongoing research at EURECOM will be presented, focusing on how advanced wireless technologies can empower aerial robotic systems, and conversely, how robotic platforms can enhance network capabilities by providing extended connectivity, environmental awareness, and accurate sensing. The talk will also showcase real-world experimentation using OpenAirInterface and the custom platform developed by the Drone4Wireless laboratory at EURECOM, offering practical insights and results at the intersection of robotics and wireless innovation with application to connectivity, sensing, localization and charting.

Min Huang, Northeastern University, China

Min Huang is currently a Full Professor with the College of Information Science and Engineering, Northeastern University, China. She received the National Science Fund for Distinguished Young Scholars in 2013, and has been a Changjiang Scholarship Chair Professor, Ministry of Education, China, since 2016. She was a Senior Visiting Scholar with the Department of Industrial and Operations Engineering, University of Michigan (Ann Arbor), Michigan, USA, in 2011. Prof. Huang is the Head of the Artificial Intelligence Department of Northeastern University, the director of the Liaoning Key Laboratory of Intelligent Science and Intelligent System. She is the recipient of eight national or provincial prizes and awards. Prof. Huang has been granted over more than 40 national and provincial projects, authored more than 200 refereed publications in international academic journals, such as IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cybernetics, published 10 books (1 translation), with her research interests including modeling, analytics, and optimization for industrial network, blockchain, chain systems, risk management, behavioral operations management, computational intelligence.

 

Title: Distributed and Trustworthy Knowledge-Defined Networking

Abstract:

As network environments grow increasingly complex and decentralized, traditional network architectures face critical challenges such as limited scalability, insufficient intelligence, and inadequate trust assurance. Addressing these challenges necessitates the development of mechanisms that enable distributed self-learning, self-adaptation, and self-optimization under privacy-preserving constraints to enhance overall network performance. To this end, we propose a distributed and trustworthy knowledge-defined networking architecture that integrates knowledge-defined networking, federated learning, and blockchain technologies. This framework facilitates distributed modeling and decentralized trust management, promoting intelligent and autonomous network evolution. Its core contribution lies in overcoming the rigidity of conventional control paradigms by fostering a flexible, scalable, and collaborative ecosystem capable of efficient and reliable resource coordination. Ultimately, this architecture establishes both the theoretical and technical foundations for sustainable, intelligent, and trustworthy next-generation networks.

Chen Tian, Nanjing University, China

Chen Tian, is currently a professor and doctoral supervisor at the School of Computer Science, Nanjing University. In 2023, he was selected for the National Science Fund for Distinguished Young Scholars. His research expertise lies in computer networks and distributed systems. He has published more than 100 papers in top academic conferences and renowned international journals in the fields of computer networks and distributed systems, such as SIGCOMM, NSDI, OSDI, FAST, SIGMOD, PPoPP, and Eurosys. He proposed a congestion management concept centered on traffic control for next-generation data center networks, designed a stateful programmable network tester with independent intellectual property rights, led the realization of large-scale parallel acceleration of open-source network simulation software, and served as the rotating chairman of the OpenNetLab international network testbed.

 

Title: Squeezing Operator Performance Potential for the Ascend Architecture
Abstract:
With the rise of deep learning, many companies have developed domain-specific architectures (DSAs) optimized for AI workloads, with Ascend being a representative. To fully realize the operator performance on Ascend, effective analysis and optimization is urgently needed. Compared to GPU, Ascend requires users to manage operations manually, leading to complex performance issues that require precise analysis. However, existing roofline models face challenges of visualization complexity and inaccurate performance assessment. To address these needs, we introduce a component-based roofline model that abstracts components to capture operator
performance, thereby effectively identifying bottleneck components. Furthermore, through practical operator optimization case studies, we illustrate a comprehensive process of optimization based on roofline analysis, summarizing common performance issues and optimization strategies. Finally, extensive end-to-end optimization experiments demonstrate significant model speed improvements, ranging from 1.07× to 2.15×, along with valuable insights from practice.