CCAI 2023 Keynote Speakers | 主旨报告


Prof. Jiangzhou Wang (王江舟 教授)
University of Kent, U.K.  

- IEEE Communications Society Leonard G. Abraham Prize (2022)
- Fellow of the Royal Academy of Engineering, UK (FREng) (2018)
- Fellow of IEEE (2017)

Biosketch: Jiangzhou Wang (IEEE Fellow) is a Professor at the University of Kent, U.K. His research interest is in mobile communications. He has published over 400 papers and 4 books. He was a recipient of the 2022 IEEE Communications Society Leonard G. Abraham Prize and the 2012 IEEE Globecom Best Paper Award. Professor Wang is a Fellow of the Royal Academy of Engineering, U.K., Fellow of the IEEE, and Fellow of the IET. He was the Technical Program Chair of the 2019 IEEE International Conference on Communications (ICC2019), Shanghai, the Executive Chair of the IEEE ICC2015, London, and the Technical Program Chair of the IEEE WCNC2013.  

Speech Title: Artificial intelligence applied to 6G mobile communications 

Abstract: Because there are many vertical industry applications, mobile communication is developing very rapidly. This lecture will discuss the application of artificial intelligence/reinforcement learning in 6G mobile communications. The lecture will explain why mobile communication requires artificial intelligence/reinforcement learning and how it works, and finally give application examples. 


Prof. Min Chen (陈敏 教授)
Huazhong University of Science and Technology (HUST), China (华中科技大学) 

Fellow of IEEE, Highly Cited Researcher from 2018 to 2021  

Biosketch: Min Chen is a full professor in School of Computer Science and Technology at Huazhong University of Science and Technology (HUST) since Feb. 2012. He is the director of Embedded and Pervasive Computing Lab, and the director of Data Engineering Institute at HUST. He is the founding Chair of IEEE Computer Society Special Technical Communities on Big Data. He was an assistant professor in School of Computer Science and Engineering at Seoul National University before he joined HUST. He is the Chair of IEEE Globecom 2022 eHealth Symposium. His Google Scholar Citations reached 33,050+ with an h-index of 87. His top paper was cited 3,700+ times. He was selected as Highly Cited Researcher from 2018 to 2021. He got IEEE Communications Society Fred W. Ellersick Prize in 2017, and the IEEE Jack Neubauer Memorial Award in 2019. He is an IEEE Fellow for his contributions to data-driven communication, caching, and computing.  

Speech Title: Large Scale Non-Disturbance Sensing in 6G Fabric Smart Space 

Abstract: In future network, the provisioning of ultra-low latency, non-intrusive and immersive service experience creates various challenges, among which large scale non-disturbance sensing is of great importance to continuously obtain multi-modal information without disturbing user. This talk introduces the development of various functional fabrics, based on which sensing and computing can meet the ultra-reliable and low-latency communication needs of sixth-generation wireless (6G) by integrating sensing units into fabric fibers to perceive user data. Various application examples for human activity capturing are given in 6G fabric smart space. 


Prof. Jiancun Fan (范建存 教授)
Xi’an Jiaotong University, China (西安交通大学信息与通信工程学院副院长)  

Biosketch: Dr. Jiancun Fan received the B.S. and Ph.D. degrees in electrical engineering from Xi'an Jiaotong University, Xi'an, China, in 2004 and 2012, respectively. From August, 2009 to August, 2011, he was a Visiting Scholar with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. From September, 2017 to December, 2017, he was a Visiting Scholar in Technische Universität Dresden (TUD), Germany. He is currently a Professor and the Associate Dean of the School of Information and Communications Engineering, Xi'an Jiaotong University, Xi’an, Shaanxi, China. He is a senior member of IEEE and a member of the China Branch of the ACM Special Interest Groups on Applied Computing (SIGAPP) Committee.
His general research interests include signal processing and wireless communications, with emphasis on MIMO communication systems, cross-layer optimization for spectral- and energy-efficient networks, practical issues in 5G and 6G systems, and machine learning application for wireless communication. In these areas, he has published over 100 journal and conference papers. He was a recipient of the Best Paper Award at the 20th International Symposium on Wireless. He won the Best Paper Award at the 20th International Symposium on Wireless Personal Multimedia Communications in 2017, the Second Prize of Excellent Paper of Shaanxi Provincial Natural Science, and the Excellent Doctoral Thesis of Xi'an Jiaotong University.  

Speech Title: Channel Estimation and Hybrid Precoding Techniques in mmWave Massive MIMO Systems 

Abstract: Millimeter wave massive MIMO can significantly improve the energy efficiency and spectral efficiency of the system by using a large number of antennas. Therefore, it is considered as the key technology of the next generation mobile communication and has received widespread attention. In millimeter wave massive MIMO wireless communication systems, as the number of antennas on both sides of the transmitter and receiver and the bandwidth increase, the number of estimated channel parameters and the processing complexity of precoding will significantly increase. Therefore, low complexity channel information acquisition algorithms and precoding algorithms are very important in promoting the practical application of millimeter wave massive MIMO. In this report, we will utilize the sparse characteristics of millimeter wave massive MIMO channels and a deep learning algorithm based on offline training to propose several low complexity channel estimation and precoding schemes. 




Assoc. Prof. Yuanwei Liu (刘元玮 副教授)
Queen Mary University of London, London, UK  

Web of Science Highly Cited Researcher 

Biosketch: {Yuanwei Liu} (S'13-M'16-SM'19, \url{}) received the PhD degree in electrical engineering from the Queen Mary University of London, U.K., in 2016. He was with the Department of Informatics, King's College London, from 2016 to 2017, where he was a Post-Doctoral Research Fellow. He has been a Senior Lecturer (Associate Professor) with the School of Electronic Engineering and Computer Science, Queen Mary University of London, since Aug. 2021, where he was a Lecturer (Assistant Professor) from 2017 to 2021. His research interests include non-orthogonal multiple access, reconfigurable intelligent surface, integrated sensing and communications, and machine learning.
Yuanwei Liu is a Web of Science Highly Cited Researcher since 2021, an IEEE Communication Society Distinguished Lecturer, an IEEE Vehicular Technology Society Distinguished Lecturer, and the academic Chair for the Next Generation Multiple Access Emerging Technology Initiative. He received IEEE ComSoc Outstanding Young Researcher Award for EMEA in 2020. He received the 2020 IEEE Signal Processing and Computing for Communications (SPCC) Technical Early Achievement Award, IEEE Communication Theory Technical Committee (CTTC) 2021 Early Achievement Award. He received IEEE ComSoc Outstanding Nominee for Best Young Professionals Award in 2021. He is the co-recipient of the Best Student Paper Award in IEEE VTC2022-Fall, the Best Paper Award in ISWCS 2022, and the 2022 IEEE SPCC-TC Best Paper Award. He serves as a Senior Editor of IEEE Communications Letters, an Editor of the IEEE Transactions on Wireless Communications and the IEEE Transactions on Communications. He serves as the Guest Editor for IEEE JSAC on Next Generation Multiple Access, IEEE JSTSP on Signal Processing Advances for Non-Orthogonal Multiple Access, IEEE Network on Next Generation Multiple Access for 6G. He serves as the Publicity Co-Chair for IEEE VTC 2019-Fall, Symposium Co-Chair for Cognitive Radio \& AI-Enabled Networks for IEEE GLOBECOM 2022 and Communication Theory for IEEE GLOBECOM 2023. He serves as the chair of Special Interest Group (SIG) in SPCC Technical Committee on signal processing Techniques for next generation multiple access, the vice-chair of SIG WTC on Reconfigurable Intelligent Surfaces for Smart Radio Environments.  

Speech Title: Simultaneously Transmitting And Reflecting Surface (STARS) for 360° Coverage 

Abstract: In this talk, the novel concept of simultaneously transmitting and reflecting surfaces (STARS) will be introduced. First, the STAR basics will be introduced. In particular, the fundamental signal modelling, performance limit characterization, practical operating protocols, and joint beamforming design will be discussed. Then, the coupled phase-shift STAR model, AI enabled STARS, and channel estimation methods will be focused. Furthermore, several promising case studies of employing STARS will be put forward, including STAR-NOMA, spatial analysis for STARS, federated learning with STARS, and STARS enabled integrated sensing and communications (ISAC). Finally, research opportunities and problem as well as commercial progress of STARS. 



Assoc. Prof. Zhongyuan Zhao (赵中原 副教授)
Beijing University of Posts and Telecommunications, China (北京邮电大学)  

Biosketch: Zhongyuan Zhao received the B.S. degree in applied mathematics and the Ph.D. degree in communication and information systems from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2009 and 2014, respectively.
He is currently an Associate Professor with BUPT. His research interests include fog computing/edge computing, content caching, and edge intelligence in wireless networks. He was a recipient of the Best
Paper Awards at the IEEE CIT 2014 and WASA 2015. He serves as an editor for IEEE COMMUNICATIONS LETTERS since 2016 and received the Exemplary Editors Award twice in 2017 and 2018, and an editor for IEEE Open Journal of the Communications Society. He was also a Guest Editor of IEEE ACCESS/China Communications/EURASIP WCN/Applied Science.  

Speech Title: Federated Learning with Non-IID Data in Wireless Networks 

Abstract: Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated learning. In this talk, some recent works with respect to federated learning in wireless networks is introduced. Firstly, the theoretical performance loss caused by non-IID data is analyzed for federated learning, which can provide us an enhanced federated averaging scheme is proposed to reduce the distribution divergence. Second, to further harmonize the distribution divergence, data sharing is associated with federated learning in wireless networks, and a joint optimization algorithm is designed to keep a sophisticated balance between the model accuracy and the cost. Finally, a model-level enhanced scheme, named ensemble federated learning, is introduced, which can mitigate the impact of non-IID data without extra communication and computation costs. 


Assoc. Prof. Fei Hao (郝飞 副教授)
Shaanxi Normal University, Xi'an, China (陕西师范大学)  

Biosketch: Dr. Hao received the B.Sc. degree in Information and Computing Science and the M.Sc. degree in Computer Software and Theory from Xihua University, China, in 2005 and 2008, respectively, and the Ph.D. degree in Computer Science and Engineering from Soonchunhyang University, South Korea, in 2016. Since 2016, he has been with Shaanxi Normal University, Xi'an, China, where he is an Associate Professor. From 2020 to 2022, he was a Marie Curie Fellow at the University of Exeter, Exeter, United Kingdom.
His research interests include social computing, soft computing, big data analytics, pervasive computing, and data mining. Dr. Hao holds a world-class research track record of publication in the top international journals and the prestigious conferences. He has published more than 100 papers in the leading international journals and conference proceedings, such as IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Services Computing, IEEE Transactions on Network Science and Engineering, IEEE Communications Magazine, IEEE Internet Computing, ACM Transactions on Multimedia Computing, Communications and Applications as well as SIGIR, GlobeCom. In addition, he was the recipient of six Best Paper Awards from CSA 2020, CUTE 2016, UCAWSN 2015, MUE 2015, IEEE GreenCom 2013 and KISM 2012 conferences, respectively. He was also the recipient of the Outstanding Service Award at FutureTech 2019, DSS 2018, and SmartData 2017, the IEEE Outstanding Leadership Award at IEEE CPSCom 2013 and the 2015 Chinese Government Award for Outstanding Self-Financed Students Abroad. Since 2017, he has joined JIPS (Journal of Information Processing Systems) editorial board, where he is currently an associate editor. He is also a member of ACM, CCF and KIPS.  

Speech Title: AFCMiner: Finding Absolute Fair Cliques from Attributed Social Networks for Responsible Computational Social Systems  

Abstract: Cohesive subgraph mining on attributed social networks is attracting much attention in the realm of graph mining and analysis. Most existing studies on cohesive subgraph mining over attributed social networks neglect the fairness of attributes, which lead to difficulties in deploying responsible applications. Towards this end, this talk first introduces the formalism of a new problem by introducing fairness into cliques model to mine the absolute fair cliques from attributed social networks. Specifically, Formal Concept Analysis (FCA) methodology is adopted to represent the given attributed social network, and extract a set of special attributed equiconcepts to further return the absolute fair maximal cliques. Then, an efficient absolute fair cliques detection algorithm AFCMiner for the cases of single-dimensional attributed social networks, multi-valued attributed social networks, as well as multi-dimensional attributed social networks, will be presented in this talk. Extensive experiments are conducted for demonstrating that the proposed AFCMiner algorithm can significantly reduce the time for finding absolute fair cliques with the correctness guarantee. Finally, a case study is also presented for uncovering the usefulness of our model. 

Comments are closed.