Speakers

Prof. Yan Yang

Southwest Jiaotong University, China

Brief: Dr. Yan Yang is currently Professor of Computing and Artificial Intelligence, Southwest Jiaotong University. She holds an Academic and Technical Leader of Sichuan Province. Her mainly research interests include artificial intelligence, big data analysis and mining, ensemble learning and multi-view learning, etc. She has undertaken more than 10 high-level projects and published more than 230 papers, one of which was selected as one of the 100 most influential international academic papers in China in 2021. She won the special award of Zhan Tianyou Railway Science and Technology Award, and first prize of computer science and technology of Sichuan Province. She also serves as the Vice Chair of ACM Chengdu Chapter, and Vice-Chairman General of Sichuan Computer Society.

Title: Spatio-Temporal Data Prediction for Smart City

Abstract: In smart cities, spatio-temporal data contains rich information and intrinsic value, which is of great significance. Through the analysis and exploration of urban spatial-temporal data, it can address typical challenges such as traffic congestion, thus providing invaluable support for the construction of smart cities. In this talk, I will introduce deep learning, multi-task learning, multi-view learning and transfer learning to fully extract the nonlinear and dynamic spatial-temporal dependencies within data. Several novel deep learning models are devised and the effectiveness of the proposed models is shown through the demands of traffic prediction.

Prof. Zhonghong Ou

Beijing University of Posts and Telecommunications, China

Brief: Ou Zhonghong, professor and doctoral supervisor of the School of Computer Science (National Demonstration Software Institute), deputy director of the Personnel Office, director of the Talent Office, 1551 talents, young scientists of the National Key R & D Program, and famous young teaching teachers in Beijing. He used to be the deputy dean of the School of Computer Science (National Demonstration Software Institute), and is currently the director of the Computer Special Committee of the Virtual Simulation Experimental Teaching Innovation Alliance. He is the deputy director of the Education Innovation and Industry-Education Integration Special Committee of the National Computer Basic Education Research Association of Higher Education. He is the deputy head of the TC11 VR/AR sub-working group of the China Communications Standardization Association. He is a standing member of the Intelligent Service Special Committee of the China Society of artificial intelligence. He is a member of the CCF Big Data/computer vision/education special committee He has undertaken a number of national key R & D programs and National Natural Science Foundation of China projects, and has published more than 80 high-level papers in IEEE TMC, TMM, TCC, ACM SenSys, etc. His research results have been reported by BBC News, ACM TechNews, The Register and other internationally renowned media. Research interests include small sample learning, cross-domain adaptation, and small object detection.

Title: Graph Learning for Personalised Recommendations
Abstarct: Personalised recommendations play a crucial role in enhancing user experience on various online platforms. Traditional recommender systems usually rely on user-item interaction and collaborative filtering techniques, which have limitations in capturing the complex dependencies and dynamics of user preferences. In recent years, with their advantages in handling non-Euclidean spatial data, graph learning methods have been widely applied. In recommender systems, graph learning can leverage the rich structural information in behavioural graphs to effectively learn complex user preferences. In the following, we first provide a comprehensive review of graph learning techniques for personalised recommendation, and then introduce cutting-edge GNN schemes, including fine-grained interactive graph neural networks for session recommendation and heterogeneous graph neural networks for news recommendation. Finally, we outline several research challenges and potential directions for future developments in graph learning for personalised recommendations.

Prof. Asad Khalil

Southwest University of Political Science & Law, China

Brief: Professor Assad Khalil skillfully integrates academic and practical experience in the fields of education, management, law into his teaching and management roles. His work in teaching, training and management, as well as his professional collaborations, is a testament to his commitment to the practical application of advanced knowledge. Professor Khalil has been recognized in several fields, including obtaining a Chinese work permit from a high-level expert. His alma mater, Chongqing University, has proudly included him in the Alumni Honor List. He has been named the most popular foreign teacher in China, which is a testament to his influence. In addition, he has also received the prestigious Chinese cultural award, Light of Civilization, as well as several other national awards.

Professor Assad Khalil is currently writing his forthcoming book, Global Diplomacy, which promises to be another important addition to his extensive work. His research interests include the Belt and Road Initiative, culture and education, international law, politics, and international relations, reflecting his broad and inclusive approach to global issues and academia.

Title: Education for the Future: Learning and Teaching for Sustainable Development in Education

A. Prof. Kaixiang Yang

South China University of Technology, China

Brief: Associate Professor Kaixiang Yang, currently an associate professor and doctoral supervisor in the School of Computer Science and Engineering, South China University of Technology. Mainly engaged in machine learning and data mining research, focusing on the analysis and application of complex multi-source heterogeneous data. His research results have been published in nearly 50 papers in important international journals and conferences such as IEEE TNNLS, TKDE, TII, TSMC-A, AAAI, etc. He has applied for/authorized more than 15 invention patents. He has served as a visiting editorial board member of many SCI journals, and won the ACM Guangzhou Rising Star Award, the second prize of CCF Natural Science, and the second prize of Wu Wenjun artificial intelligence science and technology. He served as a member of the China Computer Federation artificial intelligence and pattern recognition professional committee, and a member of the ADPRL professional committee of the Chinese Society of Automation. Presided over the National Natural Science Foundation of China, national key laboratory projects, provincial key projects, etc., as a research backbone to participate in the Ministry of Science and Technology's key R & D plans, national key fund joint projects, etc.

Title: Heterogeneous Data Mining and Application