Speakers

Prof. Zhipeng Cai

IEEE Fellow

ACM Distinguished Member

AAIA Fellow

Georgia State University, USA

Profile:  

Dr. Zhipeng Cai received his PhD and M.S. degrees in the Department of Computing Science at University of Alberta. He is currently a Professor in the Department of Computer Science at Georgia State University, and also an Affiliate Professor in the Department of Computer Information System at Robinson College of Business, as well as a Director at INSPIRE Center. Dr. Cai also leads the Innovative Computing and Networking (ICN) group. Dr. Cai's research expertise lies in Resource Management and Scheduling, High Performance Computing, Privacy, Networking, and Big Data. His research has received funding from multiple academic and industrial sponsors, including the National Science Foundation and the U.S. Department of State, and has resulted in over 100 publications in top journals and conferences, with more than 18,000 citations, including over 100 IEEE/ACM Transactions papers. He is listed as one of The World's Top 2% Scientists (2020 - 2024, published by Stanford University). Additionally, ScholarGPS ranks him in the top 0.05% of all scholars globally, acknowledging his significant scholarly contributions. Dr. Cai holds the esteemed position of Editor-in-Chief for Elsevier High-Confidence Computing Journal. He also serves as an editor for several prestigious journals, including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Vehicular Technology (TVT), IEEE Transactions on Wireless Communications (TWC), and IEEE Transactions on Computational Social Systems (TCSS). Moreover, Dr. Cai serves as a Steering Committee Co-Chair for WASA and is a Steering Committee member for COCOON (CCF B), IPCCC (CCF C), and ISBRA (CCF C). He has also chaired numerous international conferences, including ICDCS, SocialCom, and ISBRA. Dr. Cai has supervised over 24 PhD students, 15 of whom are now tenure-track faculty members in US institutions. Dr. Cai is deeply committed to computer security education and is a core member of Eureka Labs, a platform offering inquiry-based, hands-on lab activities that explore key cybersecurity concepts. This platform provides free, high-quality security education materials to both learners and educators. Since 2020, the 32 labs available on the platform received over 75, 200 views and downloads from 112 countries, averaging 2,350 views or downloads per lab. Dr. Cai is the recipient of an NSF CAREER Award and is a Fellow of IEEE.

Speech Title: TBD

Abstract: TBD

Prof.  Jiangtao Wang

National Young Talent

University of Science and Technology of China, China

Profile:  

Dr. Wang is a Professor at the School of AI and Data Science, University of Science and Technology of China (USTC). Before joining USTC, he was an Assistant Professor and Associate Professor in the UK. Dr. Wang specializes in developing AI algorithms to analyze complex healthcare data, aiming to improve healthcare delivery. He has developed advanced machine learning models for population health monitoring, diagnosis prediction, drug-drug interaction analysis, and COVID-19 severity estimation, often outperforming current methods on real-world data. 

Speech Title: Data-Knowledge Dual-Driven AI and its Applications in Population Health

Abstract: Artificial Intelligence (AI) is transforming healthcare by enabling predictive models, automated diagnostics, and personalized treatment plans. However, realizing the full potential of AI in health faces a significant challenge: the scarcity of high-quality labeled data. Many healthcare applications, such as disease prediction, drug interaction analysis, and public health monitoring, require vast amounts of data to train accurate models. Yet, health data is often limited, fragmented, or difficult to obtain due to privacy concerns, patient diversity, and the complexity of medical conditions. This "low-data challenge" poses a critical barrier to advancing AI-driven solutions in healthcare. In this talk, I explore how domain knowledge—such as medical expertise, clinical guidelines, and epidemiological insights—can play a crucial role in addressing this issue. By incorporating expert knowledge into AI models, we can enhance learning from small datasets and generate more reliable predictions. Through case studies in population health, we will demonstrate practical applications, such as the use of expert-informed models for predicting health outcomes and assessing environmental health risks. These examples showcase how combining AI techniques with domain knowledge not only helps overcome the data limitation but also produces actionable insights for real-world health challenges.

Prof. Xu Cheng

IEEE Senior Member

Tianjin University of Technology, China

Profile:  

Xu Cheng (Senior Member, IEEE) is a full professor at Tianjin University of Technology. He obtained his Ph.D. degree in Engineering from the Department of Ocean Operations and Civil Engineering, Intelligent Systems Laboratory, Norwegian University of Science and Technology (NTNU). From April 2022 to June 2023, he worked at Smart Innovation Norway as a permanent researcher. From 2023 to 2024, he received a fellowship from the Marie Skłodowska-Curie Actions and worked at Technical University of Denmark, Copenhagen, Denmark. He has published over 130 scientific papers in international journals and conferences, including 30 papers in IEEE Transactions and 1 book in Springer. He received the National Excellent Youth Foundation Award of NSFC. He organized about 20 international conferences and serves as associate editors of 3 international journals, e.g. IEEE Transactions on Automation Science and Engineering.

Speech Title: Ship As Wave buoy: Data-driven Sea State Estimation Based On Ship Motion Data

Abstract: This talk focuses on a comprehensive investigation into data-driven Sea State Estimation (SSE) by leveraging a vessel's own motion data. It presents a collection of advanced deep learning frameworks designed to overcome critical, real-world challenges inherent in this approach. The talk systematically introduces key issues including: the class imbalance of sea state data, where rare but hazardous conditions are difficult to predict; the need for model transferability between different ships and loading conditions; and the crucial demand for security and robustness against adversarial data attacks. To solve these problems, the authors introduce a suite of innovative architectures employing techniques such as densely connected convolutional networks, prototype-based classifiers, multi-scale feature learning, adversarial transfer learning, and dynamic graph networks. The efficacy of these models is rigorously validated on both public benchmarks and specialized ship motion datasets, demonstrating superior performance over existing state-of-the-art methods and providing a robust toolkit for enhancing maritime safety and efficiency.

Prof. Chanjuan Liu

Dalian University of Technology, China

Profile:  

Liu chanjuan is a professor and Ph.D. supervisor at Dalian University of Technology. Her main research focuses on multi-agent decision-making and optimization. She has authored over 60 peer-reviewed papers published in prestigious international journals and conferences, including Automatica, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and Nucleic Acids Research. She is the Principal Investigator of major grants, including the NSFC Excellent Young Scientists Fund and national key projects. She is a recipient of several honors, including the Young Elite Scientists Sponsorship Program by CAST (2018), Dalian Youth Science and Technology Star, the Liaoning Provincial Youth Science and Technology Award.

Speech Title: Intelligent Game-Theoretic Decision-Making: From Cognitive Agent Modeling to Cross-Domain Applications

Abstract: 

Game-theoretic decision-making is essential in various fields, including politics, economics, and society as a whole. With the rapid advancement of artificial intelligence technologies, intelligent game-theoretic decision-making has emerged as a crucial area of research. This presentation highlights recent advancements in this field and proposes a multi-tiered framework focused on three fundamental aspects of multi-agent decision-making in complex, dynamic environments: model construction, strategic reasoning, and algorithmic optimization.

Assoc. Prof. Por Lip Yee

University of Malaya, Malaysia

Profile:  

Lip Yee received his Ph.D. from the University of Malaya, Malaysia under the supervision of Prof. Abdullah bin Gani in 2012. Currently, he is an Assoc. Professor at the Department of System and Computer Technology, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia. He is also a senior member of IEEE. Lip Yee and his team were the first few pioneers who received IRPA, E-Science, FRGS, ERGS, PRGS, HIR and IIRG grants. He was the first person who managed to secure 2 E-Science funds with the role of PI in 2008. He was also the first person at the FCSIT who managed to secure the PRGS and ERGS grants. Besides collaborators from Malaysia, Lip Yee also has international collaborators from France, UK New Zealand, Turkey, Thailand and China. He also established his connections with his national and international collaborators with some industrial partners in Malaysia and other countries.

Speech Title: An Advanced Algorithm for Graphical Authentication: Enhancing Cognitive Usability and Mitigating Shoulder-Surfing Attacks

Abstract: 

This research explores innovative strategies in graphical authentication to combat vulnerabilities from shoulder-surfing attacks. In the contemporary data security landscape, developing robust yet user-friendly authentication mechanisms is essential. Traditional alphanumeric passwords are susceptible to brute-force attacks and can be difficult to manage due to their complexity. Graphical passwords present a compelling alternative by leveraging human cognitive abilities to recognize and recall images, patterns, and symbols. Our study assesses the effectiveness of graphical passwords in preventing shoulder-surfing threats, where malicious actors steal login credentials by observing legitimate users, posing significant privacy and security risks. We introduce advanced algorithms that balance security and usability effectively. Our empirical findings demonstrate that the proposed approach not only enhances security but also reduces login times compared to traditional methods, underscoring the efficiency and practical applicability of user authentication. In summary, this research introduces advanced innovations in graphical authentication to address shoulder-surfing challenges, streamline the user experience, and prioritize accessibility and inclusivity in modern digital settings.