Professor Yan Zhang, IEEE Fellow, is currently a Full Professor at the Department of Informatics, University of Oslo, Norway. He received a Ph.D. degree in School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore. His current research interests include: 6G networks, Internet of Things (e.g., transport, smart grid). His works in these areas have received more than 35000+ citations and H-index 98. He received the prestigious award Clarivate Analytics (previously Thomson Reuters) “Highly Cited Researcher” since 2018. He is IEEE Fellow, IET Fellow, an elected member of Academia Europaea (MAE), an elected member of Royal Norwegian Society of Sciences and Letters Academy (DKNVS), and an elected member of Norwegian Academy of Technological Sciences (NTVA). He serves as the Chair of IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing) during 2021-2023 and 2019-2021. He is IEEE VTS (Vehicular Technology Society) Distinguished Lecturer for two terms (2016-2018 and 2018-2020). He served as a symposium/track/general chair in many conferences, including IEEE/ACM IWQoS 2022, IEEE ICC 2021, IEEE SmartGridComm 2021, IEEE Globecom 2017, IEEE PIMRC 2016, and IEEE SmartGridComm 2015. He is currently serving as an Area Editor/Editor/Associate Editor of several top-ranked IEEE Transactions/Magazines: IEEE Transactions on Wireless Communications, IEEE Network Magazine; IEEE Transactions on Network Science and Engineering; IEEE Transactions on Industrial Informatics; IEEE Transactions on Sustainable Computing; IEEE Transactions on Vehicular Technology; IEEE Transactions on Green Communications and Networking; IEEE Internet of Things Journal; IEEE Systems Journal; IEEE Vehicular Technology Magazine.
https://folk.universitetetioslo.no/yanzhang/
DVP term expires December 2025
Presentations
Digital Twin
In this tutorial, we will present basic concepts related to digital twin and key enabling technologies with respect to communications, computation, machine learning, and cyber-physical optimization. We will first introduce the main concepts and challenges related to Digital Twin (DT) and we will provide a thorough perspective on why and how DT can be adapted for different applications. Then, we present a novel scenario DITEN (Digital Twin Edge Networks) and the research challenges related to offloading and edge association. In this scenario, we will focus on resource allocation, system models and optimization problems, and various offloading and edge association techniques. Next, we will present DT and machine learning to add intelligence and present our ideas on utilizing deep learning (deep reinforcement learning, federated learning) for low-latency, privacy-preservation, energy-efficiency and Quality-of-Service.
Vehicular Edge Computing and Networks
We first created the term and the research field “Vehicle Edge Computing (VEC)” in 2017, which is currently the most active research area in Internet of Vehicles. In this tutorial, we will first present the key concepts and main principles related to vehicle edge computing. Then, we will present the recent studies on computation offloading, edge caching, joint design and Blockchain for VEC. Different optimization and machine learning approaches have been exploited to address key challenges, including game theory, federated learning and deep reinforcement learning. Open research issues will be also pointed out throughout the talk.
Edge Intelligence
In this tutorial, we will present basic concepts related to edge intelligence and key enabling technologies with respect to communications, computation, machine learning, deep learning and cyber-physical optimization. We will first introduce the main concepts and challenges in the future-generation wireless mobile networks. Then, we will provide a thorough perspective on how mobile edge computing concepts can be adapted for the future networks. In this scenario, we will focus on resource allocation, models and optimization problems, and various offloading and caching techniques. Next, we will extend mobile edge computing to edge intelligence and present our ideas on utilizing deep reinforcement learning (deep Q-learning, DDPG) for data transmission, offloading and content distribution in different scenarios, e.g., intelligent transport systems.
