Keynote Speakers

Prof. Zhaocheng Wang, IEEE Fellow, IET Fellow

Tsinghua University, China

Prof. Zhaocheng Wang received his B.S., M.S. and Ph.D. degrees from Tsinghua University in 1991, 1993 and 1996, respectively. From 1996 to 1997, he was a Post Doctoral Fellow with Nanyang Technological University, Singapore. From 1997 to 1999, he was a Research Engineer/Senior Engineer with OKI Techno Centre Pte. Ltd., Singapore. From 1999 to 2009, he was a Senior Engineer/Principal Engineer with Sony Deutschland GmbH, Germany. Since 2009, he has been a Full Professor with Department of Electronic Engineering, Tsinghua University. Prof. Wang was previously the Director of Broadband Communication Key Laboratory, Tsinghua National Laboratory for Information Science and Technology from 2012 to 2019, and is currently the Director of Broadband Wireless Communication and Signal Processing Laboratory. He is a Highly Cited Researcher (Clarivate Analytics), a World’s Top 2% Scientist (Stanford University), a Fellow of the IEEE, a Fellow of the AAIA, a Fellow of AIIA and a Fellow of the IET.

Prof. Wang’s research interests include 6G wireless communications, AI-empowered wireless communications, and visible light communications. He is internationally well-known for a large number of high-quality original granted patents and publications in high-impact journals. He holds 64 granted U.S./European patents. Several granted patents have been regarded as standard essential patent (SEP) by a plurality of international standards. Besides that, he authored/co-authored five books, two of which have been selected by IEEE Series on Digital & Mobile Communication and published by Wiley-IEEE Press. Prof. Wang has published over 300 peer-reviewed papers, 17 of which have been selected as Clarivate Analytics ESI highly cited papers, 7 of which received Best Paper Awards, including IEEE ComSoc Leonard G. Abraham Prize (Best Paper Award of IEEE Journal of Selected Areas in Communications), IEEE ComSoc Asia-Pacific Outstanding Paper Award, IET Premium Award (Best Paper Award of Electronics Letters), IEEE Scott Helt Memorial Award (Best Paper Award of IEEE Transactions on Broadcasting), and several Best Paper Awards from renowned international conferences. He has garnered in excess of 20000 Google citations and his H-index is 68.

Prof. Wang is currently an Associate Editor for the IEEE Transactions on Communications, an Associate Editor for the IEEE/OSA Journal of Lightwave Technology, and an Associate Editor for the Digital Communications and Networks. He was an Associate Editor for the IEEE Transactions on Wireless Communications from 2011 to 2015, an Associate Editor for the IEEE Communications Letters from 2013 to 2016, an Associate Editor for the IEEE Systems Journal from 2019 to 2023, and an Associate Editor for the IEEE Open Journal of Vehicular Technology from 2019 to 2022. He was also Signal Processing for Optical Wireless Communications Symposium Co-Chair of IEEE GlobalSIP2015, Optical Wireless Communications Symposium Co-Chair of OECC2015 and Wireless Communications Symposium Co-Chair of IEEE ICC2013.

Title: Integrated Sensing and Communication Key Technologies for 6G Wireless Systems

Abstract: This talk introduces the inherent challenges to facilitate integrated sensing and communication (ISAC) for future 6G wireless systems. Specifically, the following key technologies are presented in detail including: ①Cross-interference coordination between communication and sensing; ②Cross-domain OFDM waveform design for ISAC wireless systems; ③Doppler-resilient sequence parameter optimization for ISAC wireless systems; ④Accurate positioning by single base station adopting sum-difference beams. From waveform perspective, this talk investigates different performance evaluation criteria for communication/sensing in depth. Leveraging the state-of-the-art 3GPP OFDM waveform signals, additional sensing function could be realized through orthogonal resource allocation and sequence design, while at the same time preserving the existing communication framework unchanged. From beam perspective, this study investigates different application scenarios for communication/sensing in depth. By way of large-scale antenna array, cross-interference between communication and sensing signals could be reduced significantly. Therefore, both communication rate and sensing accuracy could be improved. These key technologies facilitate the seamless integration of both communication and sensing functions from waveform design, hardware equipment to network architecture.

Prof. Jianguo Ma, IEEE Fellow

Zhongyuan University of Technology, China

Jianguo Ma received the doctoral degree in engineering in 1996 from Duisburg University, Duisburg, Germany. He was a faculty member of Nanyang Technological University (NTU) of Singapore from Sept 1997 to Dec. 2005 after his post-doctoral fellowship with Dalhousie University of Canada in Apr 1996 – Sept 1997. He was with the University of Electronic Science and Technology of China in Jan 2006 – Oct 2009 and he served as the Dean for the School of Electronic Information Engineering and the founding director of the Qingdao Institute of Oceanic Engineering of Tianjin University in Oct. 2009 – Aug 2016; he joined Guangdong University of Technology as a distinguished professor in Sept 2016 – Aug 2021.  Dr. Ma served as the Vice Dean for the School of Micro-Nano Electronics of Zhejiang University in Sept, 2021 – Oct 2022, Starting from 1 Nov 2022 he joins the Zhejiang Lab. His research interests are: Microwave Electronics; RFIC Applications to Wireless Infrastructures; Microwave and THz Microelectronic Systems; 

He served as the Associate Editor for IEEE Microwave and Wireless Components Letters in 2003 –2005; He was the member for IEEE University Program ad hoc Committee (2011~2013).

Dr. Ma was the Member of the Editorial Board for Proceedings of IEEE in 2013-2018

He is Fellow of IEEE for the Leadership in Microwave Electronics and RFICs Applications

Dr. Ma was serving as the Editor-in-Chief of IEEE Transactions on Microwave Theory and Techniques in 2020 –2022.

Speech Title: Internet of things and intelligent manufacturing

Abstract: My definition of  the Internet of Things (IoT) is "making othings talk." IoT has numerous applications, yet none are as crucial and urgent as its application in the manufacturing sector. Hence, IoT has always been more popular abroad than domestic unfortunately. This is because manufacturing is the cornerstone of a nation's economic development and sustainable existence, and industrial processes urgently need to 'talk', the manufacturing process of products urgently needs to 'talk', and the entire lifecycle status of products urgently needs to 'talk'! These aspects form the core of Industry 4.0, or industrial intellectualization and smart manufacturing. Moreover, the popularity of smart manufacturing abroad far exceeds that domestically. This report uses facts to explore whether our country still qualifies as a 'manufacturing powerhouse'. As a side note: it points out the pitifully low number of scientific papers published in our country, highlighting the need for a massive increase in publication.

Prof. Chong-Yung Chi, IEEE Life Fellow, AAIA Fellow

National Tsing Hua University, Taiwan, China

Chong-Yung Chi (Life Fellow, IEEE & AAIA Fellow) received the B.S. degree from Tatung Institute of Technology, Taipei, Taiwan, China in 1975, the M.S. degree from National Taiwan University, Taipei, Taiwan, China in 1977, and the Ph.D. degree from the University of Southern California, Los Angeles, CA, USA, in 1983, all in electrical engineering. 

He is currently a Professor of National Tsing Hua University, Hsinchu, Taiwan, China. He has published more than 240 technical papers (with citations more than 7400 times by Google-Scholar), including more than 90 journal papers (mostly in IEEE TRANSACTIONS ON SIGNAL PROCESSING), more than 140 peer-reviewed conference papers, 3 book chapters, and 2 books, including a textbook, Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press, 2017 (which has been popularly used in a series of invited intensive short courses at 10 top-ranking universities in Mainland China since 2010 before its publication). His current research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis, graph based learning and signal processing, and data security and privacy protection in machine learning.

Dr. Chi received 2018 IEEE Signal Processing Society Best Paper Award, entitled “Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization,” IEEE Transactions on Signal Processing, vol. 62, no. 21, Nov. 2014. He has been a Technical Program Committee member for many IEEE sponsored and cosponsored workshops, symposiums and conferences on signal processing and wireless communications, including Co-Organizer and General Co-Chairman of 2001 IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC). He was an Associate Editor (AE) for four IEEE Journals, including IEEE TRANSACTIONS ON SIGNAL PROCESSING for 9 years (5/2001-4/2006, 1/2012-12/2015), and he was a member of Signal Processing Theory and Methods Technical Committee (SPTM-TC) (2005-2010), a member of Signal Processing for Communications and Networking Technical Committee (SPCOM-TC) (2011-2016), and a member of Sensor Array and Multichannel Technical Committee (SAM-TC) (2013-2018), IEEE Signal Processing Society.

Title: Privacy-preserving Federated Clustering and Classification by CVX Optimization (CVXopt) or AI-aided CVXopt

Abstract: Federated learning (FL) has been a rapidly growing research area together with artificial intelligence (AI), where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients’ data. In this presentation, by means of the widely known differential privacy (DP) theory for privacy preservation, we present a supervised classification algorithm by AI-aided convex optimization (CVXopt) and an unsupervised clustering algorithm by CVXopt, each developed by solving a non-convex and non-smooth (NCNM) FL problem. Their unique insightful properties and some privacy and convergence analyses are also presented, that can be used for the FL algorithm design guidelines. Extensive experiments on real-world data are presented to demonstrate the effectiveness of the presented algorithms and much superior performance over state-of-the-art FL algorithms, together with the validation of all the analytical results and properties. Finally, we draw some conclusions as well as some future research explorations. 

Prof. Zuqing Zhu, IEEE Fellow

University of Science and Technology of China, China

Zuqing Zhu received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of California, Davis, in 2007. From 2007 to 2011, he worked in the Service Provider Technology Group of Cisco Systems, San Jose, California, as a Senior Engineer. In January 2011, he joined the University of Science and Technology of China, where he currently is a Full Professor in the School of Information Science and Technology. He has published 360+ papers in peer-reviewed journals and conferences. He is the Steering Committee Chair of the IEEE International Conference on High Performance Switching and Routing (HPSR), and was the Chair of the Technical Committee on Optical Networking (ONTC) in IEEE Communications Society. He has received the Best Paper Awards from ICC 2013, GLOBECOM 2013, ICNC 2014, ICC 2015, and ONDM 2018. He is a Fellow of IEEE.

Title: Machine Learning in and for Optical Data-Center Networks

Abstract: In the first part of this talk, we will first discuss the challenges on scalability, energy and manageability of data-center network (DCN) systems, and then explain why all-optical inter-connection can be a promising solution for future DCN systems. Next, we describe a novel all-optical inter-connection architecture based on arrayed waveguide grating router (AWGR) and wavelength-selective switches (WSS'), namely, Hyper-FleX-LION, explain its operation principle, and show experimental results of running distributed machine learning (DML) in a DCN in Hyper-FleX-LION. In the second part of this talk, we will explain how machine learning can be leveraged to realized knowledge-defined networking (KDN) and facilitate network automation in DCNs. Experimental results demonstrate that KDN can automatically reduce task completion time.

Prof. Zheng Yan, IEEE Fellow, IET Fellow, AAIA Fellow

Xidian University, China

Dr. Zheng Yan, Distinguished Professor at Xidian University, is an IEEE Fellow, IET Fellow, and AAIA Fellow. He is a Stanford World top 2% scientiss, an Academy Fellow of the Academy of Finland, and a highly cited researcher by Elsevier in China. Her research interests are in trust management, information and network security, privacy protection, and data analysis. He has published over 380 papers in prestigious journals and conferences worldwide, including IEEE SP, IEEE TIFS, IEEE TDSC, INFOCOM, and ICSE, with over 260 as first or corresponding author. He has authored two English books used for teaching for nearly a decade. He holds 107 international and domestic patents, including 50 PCT patents (with 30 independent inventions), with over 130 patents adopted by industry, some of which have entered international standards or are widely used. His U.S. patents are tracked by over 60 Fortune Global 500 companies. He has received numerous awards, including the Nokia Distinguished Inventor Award, three EU awards, N²Women Star in Computer Networking and Communications, IEEE TCSC Award for Excellence in Scalable Computing, IEEE TEMS Distinguished Leadership Award, 17 IEEE Outstanding Leadership and Service Awards, AALTO ELEC Impact Award, IEEE ComSoc Big Data Technical Committee Best Journal Paper, IEEE TrustCom Outstanding Paper, Shaanxi Natural Science Award, and Outstanding Doctoral Dissertation Supervisor by the Electronic Association. She founded the first IEEE Blockchain International Conference and serves as the Chair of the Steering Committee. He is an Executive Editor-in-Chief/Associate Editor/Editorial Board Member of over 60 journals, including ACM Computing Surveys, Information Fusion, IEEE IoT Journal, IEEE Network Magazine, and Information Sciences. He has served as a General Chair or Program Committee Chair for over 30 international conferences and has delivered nearly 30 keynote and invited speeches at international conferences and renowned enterprises.

Title: AI-empowered Trust and Trustworthy AI

Abstract: While artificial intelligence (AI) is contributing to the advancement of human society, it also presents us with new challenges. Its security and trustworthiness are worthy of in-depth exploration. This talk elucidates the key factors and basic requirements influencing the trustworthiness of AI, and introduces recent research achievements of my team, including a robust and explainable trust evaluation model with dynamicity support by employing graph neural networks and a stealthy and practical audio backdoor attack with limited knowledge. Finally, several insights are proposed regarding AI trust management.

Prof. Dongrui Wu, IEEE Fellow

Huazhong University of Science and Technology, China

Dongrui Wu received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.

Prof. Wu's research interests include affective computing, brain-computer interface, computational intelligence, and machine learning. He has 150 publications (6,400+ Google Scholar citations; h=39), including a book "Perceptual Computing" (Wiley-IEEE Press, 2010), and five US/PCT patents. He received the IEEE International Conference on Fuzzy Systems Best Student Paper Award in 2005, the IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the North American Fuzzy Information Processing Society (NAFIPS) Early Career Award in 2014, the IEEE Systems, Man and Cybernetics (SMC) Society Early Career Award in 2017, and the IEEE SMC Society Best Associate Editor Award in 2018. He was a finalist of the IEEE Transactions on Affective Computing Most Influential Paper Award in 2015, the IEEE Brain Initiative Best Paper Award in 2016, the 24th International Conference on Neural Information Processing Best Student Paper Award in 2017, the Hanxiang Early Career Award in 2018, and the USERN Prize in Formal Sciences in 2019. He was a selected participant of the Heidelberg Laureate Forum in 2013, the US National Academies Keck Futures Initiative (NAKFI) in 2015, and the US National Academy of Engineering German-American Frontiers of Engineering (GAFOE) in 2015. His team won the First Prize of the China Brain-Computer Interface Competition in 2019.

Prof. Wu is an Associate Editor of the IEEE Transactions on Fuzzy Systems (2011-2018; 2020-), the IEEE Transactions on Human-Machine Systems (since 2014), the IEEE Computational Intelligence Magazine (since 2017), and the IEEE Transactions on Neural Systems and Rehabilitation Engineering (since 2019). He was the lead Guest Editor of the IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence and Affective Computing, and the IEEE Transactions on Fuzzy Systems Special Issue on Brain Computer Interface. He is a Senior Member of the IEEE, a Board member and Distinguished Speaker of the NAFIPS, and a member of IEEE Systems, Man and Cybernetics Society Brain-Machine Interface Systems Technical Committee, IEEE CIS Fuzzy Systems Technical Committee, Emergent Technologies Technical Committee, and Intelligent Systems Applications Technical Committee. He has been Chair/Vice Chair of the IEEE CIS Affective Computing Task Force since 2012.

Title: Efficient Optimization of Fuzzy Systems

Abstract: Fuzzy systems have been widely used in classification and regression. However, for big data, traditional evolutionary algorithm based and full-batch gradient descent based optimization strategies become too costly. This talk first introduces functional similarity/equivalence between fuzzy systems and classical machine learning models such as radial basis function network, mixture of experts. Then, it extends their optimization techniques, such as mini-batch gradient descent, DropOut, Batch normalization and Adam, to the optimization of fuzzy systems.

Prof. Qinghua Huang

Northwestern Polytechnical University, China

Qinghua Huang is a professor at the School of Artificial Intelligence, OPtics and ElectroNics (iOPEN) at Northwestern Polytechnical University. His main research areas include multi-dimensional medical ultrasound imaging, medical data mining and intelligent analysis, as well as advanced medical robot systems. He has presided a number of Key projects funded by the Ministry of Science and Technology and the National Natural Science Foundation of China. He has published over 200 papers, with more than 120 of them being indexed by SCI, with a total of over 6,000 citations on Google Scholar and an H-index of 45.

Professor Huang has been recognized with various talent titles, including the "Leading Talents" under the WR Program (2021), the "Outstanding Talents of the New Century" awarded by the Ministry of Education (2010), the "Hundred Talents Program" Full-time Distinguished Professor in Shaanxi Province (2016), and the "Outstanding Young Scientist Fund" in Shaanxi Province (2019). 

Professor Huang has been invited to serve as a Distinguished Professor at the First Affiliated Hospital of Sun Yat-sen University (2023-2028), an AI and Big Data Expert Advisor at Huawei Technologies Co., Ltd. (2017-2019), and a Smart Healthcare Advisor at the Shanghai AI Laboratory, among other positions. He currently serves as an Associate Editor for renowned AI journals such as Pattern Recognition and Neurocomputing, as well as a Corresponding Editor for journal of Digital Medicine and Health (in Chinese). 

Title: Automated Ultrasound Imaging and Intelligent Analysis

Abstract: Current medical ultrasonic diagnosis faces challenges such as operation dependence, subjective interpretation, and low efficiency. Our research focuses on two aspects: data acquisition and analysis, aiming to promote the intelligence and precision of ultrasonic diagnosis. 

In terms of data acquisition, we explore multidimensional imaging theory and robotic autonomous acquisition technology. By leveraging the accuracy and smooth motion of robots, we enhance the consistency of image acquisition and reduce reliance on operators' experience and skills. Multidimensional imaging technology upgrades traditional narrow-angle, two-dimensional ultrasound images to wide-angle, three-dimensional images, facilitating a more intuitive observation of lesion structures and thus improving diagnostic accuracy. 

In data analysis, to address subjective interpretation, we establish a universal medical knowledge tensor theory model, solving the inference problem of flat knowledge graphs in diverse, multimodal, and multi-disease data. Furthermore, we propose a retrospective inference theory, combining multimodal human-machine knowledge to achieve traceable generalizable inference. 

To tackle the problem of low efficiency in ultrasound image diagnosis, we develop lesion tracking, ROI detection, and keyframe extraction algorithms based on ultrasound videos to assist doctors in accurately locating lesions and simplifying the image analysis process. Segmentation algorithms can also accelerate diagnosis localization speed. Additionally, the image-text multimodal surgical parameter prediction algorithm comprehensively captures information to optimize surgical settings.

Prof. Xinwang Liu, Winner of National Natural Science FoundationOutstanding, Youth Foundation

National University of Defense Technology, China

Xinwang Liu received his PhD degree from National University of Defense Technology (NUDT), China, in 2013. He is now Professor at School of Computer, NUDT. His current research interests include kernel learning, multi-view clustering and unsupervised feature learning. Dr. Liu has published 150+ peer-reviewed papers, including those in highly regarded journals and conferences such as IEEE T-PAMI, IEEE T-KDE, IEEE T-IP, IEEE T-NNLS, IEEE T-MM, IEEE T-IFS, ICML, NeurIPS, CVPR, ICCV, AAAI, IJCAI, etc. He is an Associate Editor of IEEE T-NNLS, IEEE TCYB and Information Fusion Journal. More information can be found at https://xinwangliu.github.io/.

Title: SimpleMKKM: Simple Multiple Kernel K-means

Abstract: We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. To optimize it, we equivalently rewrite the minimization-maximization formulation as a minimization of an optimal value function, prove its differenentiablity, and design a reduced gradient descent algorithm to decrease it. Furthermore, we prove that the resultant solution of SimpleMKKM is the global optimum. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error. After that, we develop extensive experiments to investigate the proposed SimpleMKKM from the perspectives of clustering accuracy, advantage on the formulation and optimization, variation of the learned consensus clustering matrix with iterations, clustering performance with varied number of samples and base kernels, analysis of the learned kernel weight, the running time and the global convergence. The experimental study demonstrates the effectiveness of the proposed SimpleMKKM by considerably and consistently outperforming state of the art multiple kernel clustering alternatives. In addition, the ablation study shows that the improved clustering performance is contributed by both the novel formulation and new optimization. Our work provides a more effective approach to integrate multi-view data for clustering, and this could trigger novel research on multiple kernel clustering. The source code and data for SimpleMKKM are available at https://github.com/xinwangliu/SimpleMKKMcodes/.