Speakers
SPEAKERS |
Speakers |
| Prof. Ljiljana Trajkovic (IEEE Fellow) Simon Fraser University, Canada Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She is a Distinguished Lecturer of the IEEE Circuits and System Society, a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society, and a Fellow of the IEEE. Title:Machine Learning for Detecting Internet Traffic Anomalies Abstract:Border Gateway Protocol (BGP) enables the Internet data routing. BGP anomalies may affect the Internet connectivity and cause routing disconnections, route flaps, and oscillations. Hence, detection of anomalous BGP routing dynamics is a topic of great interest in cybersecurity. Various anomaly and intrusion detection approaches based on machine learning have been employed to analyze BGP update messages collected from RIPE and Route Views collection sites. Survey of supervised and semi-supervised machine learning algorithms for detecting BGP anomalies and intrusions is presented. Deep learning, broad learning, gradient boosting decision tree, and reservoir computing algorithms are evaluated by developing models based on collected datasets that contain Internet worms, power outages, and ransomware events. |
| Prof. Guanglin Zhang School of Information Science and Technology,Donghua University, China Guanglin Zhang (Member, IEEE) received the B.S. degree in applied mathematics from Shandong Normal University, Jinan, China, in 2003, the M.S. degree in operational research and cybernetics from Shanghai University, Shanghai, China, in 2006, and the Ph.D. degree in information and communication engineering from Shanghai Jiao Tong University, Shanghai, in 2012. From 2013 to 2014, he was a Post-Doctoral Research Associate with the Institute of Network Coding, The Chinese University of Hong Kong. He is currently a Professor and the Vice Dean with the College of Information Science and Technology, Donghua University, Shanghai. His research interests include online algorithms, capacity scaling of wireless networks, vehicular networks, smart microgrids, and mobile edge computing. He has been the Local Arrangement Co-Chair of ACM TURC 2017 and 2019 and the Vice Technical Program Committees Co-Chair of ACM TURC 2018 and 2021. He is an Editor on the Editorial Board of IEEE/CIC CHINA COMMUNICATIONS. Title:Competitive Online Stay-or-Switch Algorithms With Minimum Commitment and Switching Cost Abstract:Online algorithms have been studied extensively in online decision, optimization and scheduling problem, e.g., resource allocation in networks, server scaling in data center, and energy scheduling in smart grid. The purpose of online algorithm is to make a wise decision based on the current available information, which is also desired to be good for the future. In this talk, we consider an online decision problem, where a decision maker has an option to buy a discount plan for his/her regular expenses. The problem is an extension of the classic Bahncard Problem, which is applicable for a wide range of online decision scenarios. The discount plan costs an immediate upfront charge plus a commitment charge per time slot. Upon expiration, the discount period can be extended if the decision maker continues paying the commitment charge, or be canceled if he or she decides not to pay the commitment charge anymore. We investigate online algorithms for the decision maker to decide when to buy the discount plan and when to cancel it without the knowledge of his/her future expenses, aiming at minimizing the overall cost. We propose a novel deterministic online algorithm which can achieve a closed-form competitive ratio upper bounded by 4. We further propose a randomized online algorithm with a smaller competitive ratio and two variants tailored for average-case inputs and time-varying parameters, respectively. Lastly, we evaluate our algorithms against state-of the-art online benchmark algorithms in two real-world scenarios. |
| Prof. Yang Yang, IEEE Fellow Professor, The Hong Kong University of Science and Technology (Guangzhou), China Adjunct Professor, Peng Cheng Laboratory, China Dr. Yang Yang is currently a Professor with the Internet of Things (IoT) Thrust, Information Hub, and the Acting Dean of College of Education Sciences, at the Hong Kong University of Science and Technology (Guangzhou), China. He is also an adjunct professor with the Department of Broadband Communication at Peng Cheng Laboratory, the Chief Scientist of IoT at Terminus Group, and a Senior Consultant at Shenzhen Smart City Technology Development Group, China. Before joining HKUST (Guangzhou), he has held faculty positions at the Chinese University of Hong Kong, Brunel University, U.K., University College London (UCL), U.K., CAS-SIMIT, and ShanghaiTech University, China. Yang's research interests include IoT technologies and applications, multi-tier computing networks, 5G/6G mobile communications, intelligent and customized services, and advanced wireless testbeds. He has published more than 300 papers and filed more than 120 technical patents in these research areas. Yang received his BEng and MEng degrees from Southeast University, China, in 1996 and 1999, respectively; and the PhD degree from the Chinese University of Hong Kong in 2002. He is a Fellow of the IEEE. Title:Network AI: Enabling 6G Pervasive Intelligence Abstract:Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this talk, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system’s overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions. |
| Prof. Shugong Xu, IEEE Fellow Shanghai University, China Shugong Xu is an IEEE Fellow, a distinguished professor at Shanghai University, and head of the Shanghai Institute for Advanced Communication and Data Science (SICS). He owns over 40 issued US/WO/CN patents and published more than 140 peer-reviewed research papers during his over 20 years career in research (over 15 years in industrial research labs). He was awarded "National Innovation Leadership Talent" from China government in 2013 and selected as IEEE Fellow in 2015. Moreover, prof. Xu won the 2017 Award for Advances in Communication from IEEE Communication Society. His research interests include V2X, wireless communication systems, and machine learning, etc. Title:AI+6G: From LLM to RAN Evolution Abstract：Among many new features of 5G and 5G-advance (5G -A) , AI support is NOT considered significant. However, in the 6G vision, AI capability has been considered one of the critical new features and capabilities, along with sensing capability etc. In this talk, we will discuss the recent advances in AI such as LLM, and its impact to the future RAN evolution etc.. We will discuss as well the new challenges and oppurtunities in the related fields. |
| Prof. Meixia Tao, IEEE Fellow Shanghai Jiao Tong University, China Meixia Tao is a Professor with the Department of Electronic Engineering, Shanghai Jiao Tong University, China. She received the B.S. degree in electronic engineering from Fudan University, Shanghai, China, in 1999, and the Ph.D. degree in electrical and electronic engineering from Hong Kong University of Science and Technology in 2003. Her current research interests include wireless edge learning, coded caching, reconfigurable intelligence surfaces, and semantic communications. She receives the 2019 IEEE Marconi Prize Paper Award, the 2013 IEEE Heinrich Hertz Award for Best Communications Letters, the IEEE/CIC International Conference on Communications in China (ICCC) 2015 Best Paper Award, and the International Conference on Wireless Communications and Signal Processing (WCSP) 2012 and 2022 Best Paper Awards. She also receives the 2009 IEEE ComSoc Asia-Pacific Outstanding Young Researcher award. Dr. Tao is an Associate Editor of the \textsc{IEEE Transactions on Information Theory} and an Editor-at-Large of the \textsc{IEEE Open Journal of the Communications Society}. She served as a member of the Executive Editorial Committee of the \textsc{IEEE Transactions on Wireless Communications} during 2015-2019. She was also on the Editorial Board of several other journals as Editor or Guest Editor, including the \textsc{IEEE Transactions on Communications} and \textsc{IEEE Journal on Selected Areas in Communications}. She also served as the TPC Co-Chair of IEEE ICC 2023. Title: Federated Edge Learning: Communication-Efficient Designs and Applications in Wireless Networks Abstract:Traditional artificial intelligence (AI) applications deployed in cloud data centers require extensive data acquisition, transmission, and processing, causing significant challenges in latency, energy, and privacy. FEderated Edge Learning (FEEL) emerges as a disruptive learning framework to address these issues by leveraging the sensing, computation, and communication capabilities at the network edge. FEEL allows collaborative training of global AI models across geographically distributed edge devices without accessing local private datasets by exchanging only model parameters. FEEL facilitates many emerging intelligent edge services promised by 6G, such as autonomous driving, and immersive communications. Despite its advantages, FEEL faces several key challenges, such as limited on-device computation capacities, heterogenous data distribution, and scarce radio resources. This talk will present our recent research progress towards communication-efficient and high-performance FEEL, covering topics like fundamental limits of communication efficiency, over-the-air model aggregation, federated multi-task learning, and federated knowledge distillation. Applications of FEEL for the design and optimization of wireless communication networks, including wireless D2D network power control and cell-free massive MIMO precoding, will also be discussed. |
| Prof. Dr. Alexey Vinel, IEEE Senior Member Karlsruhe Institute of Technology (KIT), Germany
Alexey Vinel is a professor at the Karlsruhe Institute of Technology (KIT), Germany. Previously he was a professor at the University of Passau, Germany. Since 2015, he has been a professor at Halmstad University, Sweden (now part-time). He received the Ph.D. degree from the Tampere University of Technology, Finland in 2013. He has been the Senior Member of the IEEE since 2012. His areas of interests include vehicular communications and networking, cooperative automated and autonomous driving, future smart mobility solutions. Title:Autonomous Vehicles: Wireless Networking for Cooperative Maneuvering Abstract:Elon Musk twitted some time ago that he had realized that self-driving cars are a "hard problem". We believe that enabling communication between the vehicles is an essential necessary step for cracking this problem in context of fully autonomous urban driving. We will share some of our recent research results on autonomous vehicles with a focus on inter-vehicular networking and respective cooperative driving functionalities. We will explain the approach of assessing the safety of the cooperative driving functions by coupling the quality of radio communications to the likelihood of a rear-end collision. |
