Keynote Speakers

Prof. Carlos Artemio Coello Coello

IEEE Fellow,  Editor-in-Chief, IEEE Transactions on Evolutionary Computation

Department of Computer Science CINVESTAV-IPN, Mexico

Professor Carlos Artemio Coello Coello received a PhD in Computer Science from Tulane University (USA) in 1996. He is currently full professor with distinction at CINVESTAV-IPN in Mexico City, Mexico. He has published over 380 papers in international peer-reviewed journals,book chapters, and conferences. He has also co-authored the book Evolutionary Algorithms for Solving Multi-Objective Problems, which is now in its Second Edition (Springer, 2007) and has co-edited the book Applications of Multi-Objective Evolutionary Algorithms (World Scientific, 2004). His publications currently report over 23,000 citations, according to Google Scholar (his h-index is 61).

He received the 2007 National Research Award (granted by the Mexican Academy of Science) in the area of exact sciences and, since January 2011, he is an IEEE Fellow for "contributions to multi-objective optimization and constraint-handling techniques."

He is also the recipient of the prestigious 2013 IEEE Kiyo Tomiyasu Award and of the 2012 National Medal of Science and Arts in the area of Physical, Mathematical and Natural Sciences (this is the highest award that a scientist can receive in Mexico). He also serves as associate editor of the journals Evolutionary Computation, IEEE Transactions on Evolutionary Computation, Computational Optimization and Applications and Applied Soft Computing.

Speech TItle: What is Missing in Evolutionary Optimization?

Abstract: In this talk, I'll provide some thoughts about my view of a field in which I have worked during almost 30 years. Besides mentioning some relevant research topics related to both single- and multi-objective optimization that are worth exploring in the next few years (e.g., dynamic problems, high dimensionality, expensive objective functions, etc.), I'll provide a more general view of the field, sharing my views about the sort of research work which I believe that is needed today so that we can start switching from producing to understanding.

Prof. Guangjie Han

IEEE Fellow, IET/IEE Fellow, AAIA Fellow

 Hohai University, China

Guangjie Han (Fellow, IEEE) is currently a Professor with the Department of Internet of Things Engineering, Hohai University, Changzhou, China. He received his Ph.D. degree from Northeastern University, Shenyang, China, in 2004. In February 2008, he finished his work as a Postdoctoral Researcher with the Department of Computer Science, Chonnam National University, Gwangju, Korea. From October 2010 to October 2011, he was a Visiting Research Scholar with Osaka University, Suita, Japan. From January 2017 to February 2017, he was a Visiting Professor with City University of Hong Kong, China. From July 2017 to July 2020, he was a Distinguished Professor with Dalian University of Technology, China. His current research interests include Internet of Things, Industrial Internet, Machine Learning and Artificial Intelligence, Mobile Computing, Security and Privacy. Dr. Han has over 500 peer-reviewed journal and conference papers, in addition to 160 granted and pending patents. Currently, his H-index is 65 and i10-index is 284 in Google Citation (Google Scholar). The total citation count of his papers raises above 15500+ times.

Dr. Han is a Fellow of the UK Institution of Engineering and Technology (FIET). He has served on the Editorial Boards of up to 10 international journals, including the IEEE Systems, IEEE/CCA JAS, IEEE Network, etc. He has guest-edited several special issues in IEEE Journals and Magazines, including the IEEE JSAC, IEEE Communications, IEEE Wireless Communications, IEEE Transactions on Industrial Informatics, Computer Networks, etc. Dr. Han has also served as chair of organizing and technical committees in many international conferences.He has been awarded 2020 IEEE Systems Journal Annual Best Paper Award and the 2017-2019 IEEE ACCESS Outstanding Associate Editor Award. He is a Fellow of IEEE.

Speech TItle: Multi-Dimensional Dynamic Trust Management Mechanism in Underwater Acoustic Sensor Networks

Abstract: The underwater acoustic sensor network (UASN) is the core module to realize the "smart ocean". At present, the UASN has not yet fully played its role in the complex water environment. The fundamental reason lies in the lack of effective methods to ensure network security and reliable data transmission. This report mainly introduces the team's research work on the trust management mechanism of UASNs. The main research contents include: 1) Intrusion detection algorithm based on energy consumption prediction model; 2) Multi-dimensional trust calculation algorithm based on fuzzy theory; 3) Trust evaluation algorithm based on cloud theory; 4) Trust cloud migration mechanism based on AUV; 5) Trust update mechanism based on reinforcement learning; 6) Anomaly-resilient trust model based on isolation forest. The research results have important theoretical value and practical significance for exploring the security technology and application of UASNs.

Prof. Xiuzhen Cheng

IEEE Fellow, CSEE Fellow, AAIA Fellow

Shandong University, China

Dr. Xiuzhen Cheng is a Professor of Computer Science at Shandong University. From 2002 to 2020, she was a faculty member at The George Washington University, USA. She served as a Program Director at NSF from April 2006 to October 2006 (full time) and from April 2008 to April 2010 (part-time). Her research focuses on the broad area of distributed and trusted computing, particularly in blockchain computing, edge computing, and IoT security. Dr. Cheng is the founder and steering committee chair of the International Conference on Wireless Algorithms, Systems, and Applications (WASA, launched in 2006), and the founding EiC of the High-Confidence Computing Journal (launched in 2021).  She served/is serving on the editorial boards of several technical journals (e.g. IEEE Transactions on Computers and IEEE Transactions on Wireless Communications) and the technical program committees of many professional conferences/workshops (e.g. ACM Mobihoc, ACM Mobisys, IEEE INFOCOM, IEEE ICDCS, IEEE/ACM IWQoS). She also chaired several international conferences (e.g. ACM Mobihoc'14). Dr. Cheng is a Fellow of IEEE, a Fellow of CSEE (Chinese Society for Electrical Engineering), and a Fellow of AAIA (Asia-Pacific Artificial Intelligence Association). Her current H-Index is 65, and the total number of Google Citations is 18,000+.

Speech TItle: Decentralized Storage Networks

Abstract: Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers to contribute their unused storage spaces and ensure strong security. However, pioneering DSNs still face challenges in many aspects such as data privacy, proof of storage complexity, multi-version file management, and file indexing. In this talk, we will report two of our recent works, namely FileDAG and FileDES, which intend to overcome the above problems in designing DSNs. Specifically, FileDAG is built on a DAG-based blockchain to support file-level deduplication in storing multi-versioned files while relying on a two-layer DAG-based blockchain ledger to provide flexible and storage-saving file indexing;  FileDES (Decentralized Encrypted Storage) extends FileDAG from multiple aspects, incorporating data security, system scalability, and proof succinctness respectively with file encryption, a lightweight proof of encrypted storage algorithm (PoES), and a succinct multi-version verification method. Open research challenges and future research directions will also be discussed.

Prof. Zuqing Zhu

IEEE Fellow

Director of INFINITE LAB,

University of Science and Technology of China (USTC), 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 the Chair of the Technical Committee on Optical Networking (ONTC) in ComSoc. 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 and a Senior Member of Optica (formally OSA).

Speech 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. Xiangjian (Sean) He

University of Nottingham Ningbo, China

Professor Xiangjian (Sean) He is a National Talent of China with a Chair Professor title. He is currently the Faculty’s Research Groups Lead, a Deputy Head of Computer Science School and the Director of Computer Vision and Intelligent Perception Laboratory at the University of Nottingham Ningbo China (UNNC). He is in list of the 'World Top 2% Scientists' reported by Stanford University in 2022and 2023.  His publications include ESI highly cited papers, papers in prestigious journals and top-tier international conferences such as TPAMI, ACM Computing Surveys, TMM, CVPR, AAAI, ACL, ECCV, ACM MM etc. He was the Professor of Computer Science and the Leader of Computer Vision and Pattern Recognition Laboratory at the University of Technology Sydney (UTS) from 2011-2022.  He was an IEEE Signal Processing Society Student Committee member. He was involved in a team receiving a UTS Chancellor's Award for Research Excellence through Collaboration in 2018. He has been awarded 'Internationally Registered Technology Specialist' by International Technology Institute (ITI). He led the UTS and Hong Kong Polytechnic University (PolyU) joint research project teams winning the 1st Runner-Up prize for the 2017 VIP Cup, and the champion for the 2019 VIP Cup, awarded by IEEE Signal Processing Society. He has been carrying out research mainly in the areas of computer vision, data analytics and machine learning in the previous years. He has recently been leading his research teams for deep-learning-based research for various applications. He is currently an Associate Editor of three journals and has played various chair roles in many international conferences such as ACM MM, MMM, ICDAR, IEEE BigDataSE, IEEE BigDataService, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE ICPR and IEEE ICARCV.       

Speech TItle: Big Data, Machine Learning and Computer Vision

Abstract: Big data are in all science and engineering domains. Analysis of them requires novel learning techniques to address the various challenges. This talk will briefly introduce the basic concepts of machine learning and give a brief survey of the research on machine learning for big data processing. Some promising learning methods in recent studies will be highlighted. Then, the challenges and possible solutions of machine learning for big data will be presented. Following that, the applications in computer vision, image and signal processing, Internet of Things, etc. will be investigated and various deep learning network models will be demonstrated for various applications such as crowd counting, image segmentation, traffic prediction, object tracking, etc.