SPEAKERS |
Keynote Speaker
Prof. Xiaoli Li, IEEE Fellow, Nanyang Technological University, Singapore
BIO: Xiaoli is currently the Department Head and Senior Principal Scientist at the Institute for Infocomm Research, A*STAR, Singapore. He also serves as an adjunct full professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. With a diverse range of research interests, Xiaoli focuses on cutting-edge areas such as AI, data mining, machine learning, and bioinformatics. His contributions to these fields are evident through his extensive publication record, boasting over 360 peer-reviewed papers, and the recognition he has received, including over ten best paper awards. He has been serving as Editor-in-chief of the Annual Review of Artificial Intelligence and an Associate Editor for prestigious journals like IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems, as well as conference chairs and area chairs of leading AI, machine learning, and data science conferences, such as AAAI, IJCAI, ICLR, NeurIPS, KDD, ICDM etc. Beyond academia, Xiaoli possesses extensive industry experience, where he has successfully spearheaded over 10 R&D projects in collaboration with major industry players across diverse sectors, such as aerospace, telecom, insurance, and professional service companies. Xiaoli is an IEEE Fellow and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA). He has been recognized as one of the world's top 2% scientists in the AI domain by Stanford University, Clarivate's Highly Cited Researcher, and one of the top ranked computer scientists by Research.com.
Title: AI-Driven Big Time Series Data Analytics
Abstract: The exponential growth of sensor deployments across industries such as manufacturing, aerospace, transportation, and education presents unprecedented opportunities for leveraging AI in time-series data analytics. This keynote highlights state-of-the-art AI solutions powering real-world applications, including predictive maintenance, and real-time decision-making. In manufacturing and aerospace, AI-driven analytics enhance operational efficiency by enabling predictive maintenance, reducing downtime, and improving machine remaining useful life predictions. Key challenges, such as achieving high accuracy, compressing models for edge deployment, and adapting to diverse industrial domains, will be discussed. In transportation, real-time AI analytics revolutionize smart traffic management, promoting safety and optimizing resource allocation. Meanwhile, in education, the rise of online learning platforms generates vast time-series data, paving the way for personalized and adaptive learning experiences. From knowledge tracing to predicting student performance and identifying at-risk learners, AI enables timely interventions and fosters tailored learning pathways. Join us to explore how AI is revolutionizing industries and education, driving innovation, and enhancing global competitiveness in today’s data-centric era.
Prof. Yong Tang, South China Normal University, China
BIO: Yong Tang is the founder of SCHOLAT and Professor of the School of Computer Science at South China Normal University. He got his BS and MSc degrees from Wuhan University in 1985 and 1990 respectively, and PhD degree from University of Science and Technology of China in 2001, all in computer science. Before joined SCNU in 2009, he was vice Dean of School of Information of Science and Technology at Sun Yat-Sen University. He has published more than 200 papers and books. He has supervised more than 50 PhD students since 2003 and more than 200 Master students since 1996. His main research areas include data and knowledge engineering, social networking and collaborative intelligent applications. For more information please visit https://scholat.com/ytang.
Title: Cooperative Intelligent Applications based on SCHOLAT big Data
Abstract: Social networks have transformed the way people live and work. However, social networks are a double-edged sword. On the one hand, they facilitate communication among people; on the other hand, irrelevant information can disrupt life and work. Against the backdrop of the social collaboration needs in scientific research and teaching, we have created a social network for scholars (SCHOLAT), providing scholars with an independent academic space and a trustworthy collaboration platform. Currently, it has over 200,000 registered users (including more than 5,000 domestic and foreign institutions, covering all 985 universities), more than 10,000 research and teaching groups, as well as hundreds of millions of academic entities and their relationships. It has become a unique producer of big data in scientific research and teaching, and can provide a data and knowledge foundation for academic research and application development. This report mainly includes: 1) Research background and scientific issues; 2) SCHOLAT core and services; 3) Research on SCHOLAT big data, knowledge graph, and scholar large model; 4) SCHOLAT data foundation and SCHOLAT+ application ecosystem. Finally, several recent application achievements will be shared.
Prof. Guochao Peng, Sun Yat-Sen University, China
BIO:Prof. Guochao Peng is a Full Professor in Information Systems and Assistant to the Dean in the School of Information Management, as well as the Vice Dean of Big Data Institute, at Sun Yat-Sen University, China. He holds a BSc in Information Management (1st Class Honours) and a PhD in Information Systems (IS), both from the University of Sheffield, UK. He has been the Principal Investigator (PI) or co-PI of 20 research grants funded by UK’s EPSRC, ESRC, China’s NSFC, industrial partners, University of Sheffield, and Sun Yat-Sen University, totaling over £2 million. Prof Peng has over 160 publications in the forms of high-quality journal papers, books, book chapters, full conference papers, and conference proceedings. He has also been the founder and chair of a number of international conferences in information systems and smart cities.
Title: Realizing the power of AI: the Importance of Data, Human Needs and Scenes
Abstract: Artificial Intelligence (AI) has emerged as a revolutionary force, transforming industries and redefining the way we live and work. From intelligent virtual assistants to advanced autonomous vehicles, AI's potential seems limitless. However, to truly harness the power of AI and create meaningful and impactful applications, three key elements play a crucial role: data, human needs, and scenes. Understanding and integrating these components is essential for the successful development and deployment of AI technologies.
Prof. Quanxin Zhu, Hunan Normal University, China
BIO:Professor Quanxin Zhu received the Ph.D. degree from Sun Yatsen (Zhongshan) University, Guangzhou,China, in 2005. He is currently a professor of Hunan Normal University, and he has obtained the Alexander von Humboldt Foundation of Germany. Professor Zhu is Distinguished professor of Furong scholars in Hunan Province, Leading talent of scientific and technological innovation in Hunan Province, and deputy director of the Key Laboratory of computing and stochastic mathematics of the Ministry of education. Professor Zhu is a Highly Cited Scientist in the world, in 2018-2022. Also, Professor Zhu is a senior member of the IEEE and he is the Lead Guest Editor of several international journals. He is an associate editor of six international SCI journals including IEEE Transactions on Automation Science and Engineering.
He has obtained the first prize of Hunan Natural Science Award in 2021, and the list of top two percent scientists in 2020-2022. Professor Zhu has obtained 2011 Annual Chinese ``One Hundred The Most Influential International Academic Paper" Award and has been One of most cited Chinese researchers in 2014-2022, Elsevier. Professor Zhu is a reviewer of more than 50 other journals and he is the author or coauthor of more than 300 journal papers.
Topic: Event-triggered control problems of stochastic nonlinear delay systems
Abstract: In this talk, we introduce the the event-triggered feedback control problem of stochastic nonlinear delay systems with exogenous disturbances. By introducing the notation of input-to-state practical stability and an event-triggered strategy, we establish the input-to-state practically exponential mean-square stability of the suggested system. Moreover, we investigate the stabilization result by designing the feedback gain matrix and the event-triggered feedback controller, which is expressed in terms of linear matrix inequalities. Also, the lower bounds of inter-execution times by the proposed event-triggered control method are obtained. Finally, an example is given to show the effectiveness of the proposed method. Compared with large number of results for discrete-time stochastic systems, only a few results have appeared on the event-triggered control for continuous-time stochastic systems. In particular, there has been no published papers on the event-triggered control for continuous-time stochastic delay systems. Our work is a first try to fill the gap on the topic.
Prof. Hong-Liang Dai, Guangzhou University, China
Bio: Hong-Liang Dai is currently a professor in the Department of Statistics at Guangzhou University, a Distinguished Professor of Guangzhou Scholars, a doctoral supervisor, and a postdoctoral mentor. He also serves as the head of the Data Science and Big Data Technology program. In June 2003, he graduated with a master's degree in Applied Mathematics from WuHan University. In June 2013, he obtained his Ph.D. in Applied Mathematics from Sun Yat-Sen University. In November 2015, he was awarded the title of Professor of Artificial Intelligence. He is an expert reviewer for the National Social Science Fund project and an appraiser for its outcomes. He is a member of IEEE, ACM, and CCF. He is a standing director of the History and Culture Branch of the Chinese Society for Field Statistics Research, a board member of the Data Science and Artificial Intelligence Branch, a board member of the Educational Statistics and Management Branch, a standing director of the Guangdong Computational Mathematics Society, and a committee member of the Big Data Professional Committee of the Guangdong Computer Society. He is an anonymous reviewer for several renowned academic journals including SCI Journal 《IEEE Transactions on Neural Networksand Learning Systems》,《IEEE Transactions on Fuzzy Systems》,《IEEE Transactions on Knowledge and Data Engineering》,《Pattern Recognition》. He has served as an expert reviewer for science and technology projects and awards at the provincial and municipal levels, including those from the Ministry of Education, Guangdong Province, Jiangxi Province, Jiangsu Province, Shandong Province, Guangzhou City, and Foshan City. In recent years, he has been engaged in research work in machine learning and big data analysis, publishing over 60 research papers in international prestigious academic journals such as 《Pattern Recogtion》, 《Expert Systems With Applications》, 《Knowledge-Based Systems》, 《Applied Soft Computing》, 《Computers & Industrial Engineering》, 《Information Sciences》, among which 22 papers have been indexed by JCR SCI Q1,13 by CAS SCI Q1, and 5 ESI highly cited and hot papers. He holds 3 invention patents, 2 application patents, and 4 software copyrights. His research projects include sub-projects of major national social science fund projects, general national social science fund projects, key national statistical science projects, general humanities and social sciences fund projects of the Ministry of Education, and general natural science fund projects of Guangdong Province. He has received the titles of "Most Popular Teacher" at the 11th and 12th sessions of Guangzhou University and "Outstanding Young Teacher" in the third youth teacher teaching competition at Guangdong University of Finance and Economics.
Title: Adaptive multiple kernel clustering using low-rank representation
Abstract: Multiple kernel clustering (MKC) effectively extracts intrinsic and complementary information from data by integrating diverse kernel functions. The allocation of kernel weights is crucial for MKC performance and is closely related to the relationships among kernel matrices. However, it is very difficult to fully capture the intricate relationships among high-dimensional matrices because previous research mostly relies on predefined metrics to characterize the correlation among kernel matrices. To address this challenge, a novel MKC model called AMKC-LRR is proposed that adaptively learns the interrelations among kernel matrices using low-rank representation and unifies this learning process with the clustering task within an optimization framework. Furthermore, an effective alternate optimization algorithm is designed to solve the resulting problem. Extensive experiments and statistical tests conducted on twelve commonly used benchmark datasets show that our proposed model performs favorably in comparison to state-of-the-art MKC methods.