Speakers

Prof. Irwin King,  IEEE Fellow, INNS Fellow, AAIA Fellow, ACM Distinguished Member

The Chinese University of Hong Kong, China

BIO: Professor Irwin King is a distinguished professor at the Department of Computer Science & Engineering, The Chinese University of Hong Kong. His research interests span various areas, including machine learning, social computing, AI, and data mining. Professor King has a significant publication record in top venues and serves as an editorial board member for numerous international publishers. He is an IEEE, INNS, AAIA and HKIE Fellow and ACM Distinguished Member. Throughout his career, Professor King has held leadership roles in prominent conferences and societies. He has served as the President of the International Neural Network Society (INNS) and has taken on key positions, such as General Co-chair, for many premier international conferences. Additionally, he is the Director of the Machine Intelligence and Social Computing Lab and the Trustworthy Machine Intelligent Joint Lab.  Professor King obtained his B.Sc. from Caltech and his M.Sc. and Ph.D. degrees in Computer Science from USC.

Speech Title: Multimodal Foundation and Large Language Models: Applications, Challenges, and Future Directions

Abstract: In recent years, the field of artificial intelligence has witnessed significant advancements in multimodal foundation and large language models. This keynote will provide an exploration of these models, focusing on their applications across various domains such as science, robotics, recommender systems, and watermarking. We will discuss the current trends in multimodal models, highlighting their growing importance in understanding and processing complex information. Additionally, we will delve into the challenges faced by these models, such as scalability, trustworthiness, and explore potential future directions for the field. By examining innovative approaches to improve these challenges and considering the impact of emerging technologies, we aim to inspire further research and innovation in this rapidly evolving field.

Prof. Haijun Zhang, IEEE Fellow

University of Science &Technology Beijing, China

BIO: Haijun Zhang (Fellow, IEEE) is currently a Full Professor at University of Science and Technology Beijing, China. He was a Postdoctoral Research Fellow in Department of Electrical and Computer Engineering, the University of British Columbia (UBC), Canada. He serves/served as Track Co-Chair of VTC Fall 2022 and WCNC 2020/2021, Symposium Chair of Globecom’19, TPC Co-Chair of INFOCOM 2018 Workshop on Integrating Edge Computing, Caching, and Offloading in Next Generation Networks, and General Co-Chair of GameNets’16. He serves as an Editor of IEEE Transactions on Wireless Communications, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Communications. He received the IEEE CSIM Technical Committee Best Journal Paper Award in 2018, IEEE ComSoc Young Author Best Paper Award in 2017, IEEE ComSoc Asia-Pacific Best Young Researcher Award in 2019. He is a Distinguished Lecturer of IEEE and IEEE Fellow.

Speech Title: Resource Optimization in 6G

Abstract: This talk will identify and discuss technical challenges and recent results related to 6G mobile resource optimization. The talk is mainly divided into four parts. The first part will introduce 6G mobile networks, discuss about the 6G mobile networks architecture, and provide some main technical challenges in 6G mobile networks. The second part will focus on the issue of resource management in 6G networks and provide different recent research findings that help to develop engineering insights. The third part will address the machine learning and deep learning method based future 6G networks and address some key research problems. The last part will summarize by providing a future outlook of 6G mobile network optimization.

Prof. Qinmin Yang, IEEE Senior Member

Zhejiang University, China

BIO: Qinmin Yang received the Bachelor's degree in Electrical Engineering from the Civil Aviation University of China, Tianjin, China, in 2001; the Master of Science Degree in Control Science and Engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2004; and the Ph.D. degree in Electrical Engineering from the University of Missouri-Rolla, MO, USA, in 2007. From 2007 to 2008, he was a postdoctoral research associate at the University of Missouri-Rolla. From 2008 to 2009, he was a system engineer with Caterpillar, Inc. From 2009 to 2010, he was a post-doctoral research associate at the University of Connecticut. Since 2010, he has been with the State Key Laboratory of Industrial Control Technology, the College of Control Science and Engineering, Zhejiang University, China, where he is currently a professor. He has also held visiting positions at the University of Toronto and Lehigh University.

Speech Title: Theoretical research and practice in intelligent control design for wind energy

Abstract: Wind energy has been considered to be a promising alternative to current fossil-based energies. Large-scale wind turbines have been widely deployed to substantiate the renewable energy strategy of various countries. In this talk, challenges faced by control community for high reliable and efficient exploitation of wind energy are discussed. Advanced controllers are designed to (partially) overcome problems, such as uncertainty, intermittence, and intense dynamics. Theoretical results and attempts for practice are both present.

Prof. Xiangjian He

University of Nottingham Ningbo, China

BIO: Professor Xiangjian (Sean) He received his PhD in Computer Science from the University of Technology Sydney in 1999. He is currently the 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 2022.

He was the Professor of Computer Science and the Leader of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC) 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. In 2021, the team, PolyUTS, led by Prof Lam of PolyU and co-led by Prof He of UTS again won the 1st Runner-Up award for the 2021 VIP Cup.

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 human behavious recognition, human counting and density estimation, tiny object detection, biomedical applications, saliency detection, natural language processing, cybersecurity, face and face expression recognition, road sign detection, license plate recognition, etc. He 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: Salient Object Detection

Abstract: Salient object detection aims to mimic the human visual system and cognition mechanisms to identify and segment salient objects. However, due to the complexity of these mechanisms, current methods are not perfect. Accuracy and robustness need to be further improved, particularly in complex scenes with multiple objects and background clutter. In this talk, two methods are presented. The first approach estimates depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Although many other RGB-D saliency models have also been proposed, they require to acquire depth data, which are expensive and not easy to obtain. The second approach adopts the boundary sensibility, content integrity, iterative refinement, and frequency decomposition mechanisms. A multi-level hybrid loss is designed to guide the network to learn pixel-level, region-level, and object-level features. Comprehensive evaluations on challenging benchmark datasets show the achievements of state-of-the-art results of the proposed approaches.