Keynote Speaker

Prof. Bhavani Thuraisingham
ACM/IEEE/AAAS/NAI Fellows
The University of Texas at Dallas, USA
Title: Trustworthy Artificial Intelligence for Securing Transportation Systems
Abstract:
Artificial Intelligence (AI) techniques ae being applied to numerous applications from Healthcare to Cyber Security to Finance. For example, Machine Learning (ML) algorithms are being applied to solve security problems such as malware analysis and insider threat detection. However, there are many challenges in applying ML algorithms for various applications. For example, (i) the ML algorithms may violate the privacy of individuals. This is because we can gather massive amounts of data and apply ML algorithms on the data to extract highly sensitive information. (ii) ML algorithms may show bias and be unfair to various segments of the population. (iii) ML algorithms themselves may be attacked possibly resulting in catastrophic errors including in cyber physical systems such as transportation systems.
In this presentation, we discuss our research we are conducting as part of the USDOT National University Technology Center TraCR (Transportation Cybersecurity and Resiliency) led by Clemson University. In particular, we describe (i) the application of federated machine learning techniques for detecting attacks in transportation systems; (ii) publishing synthetic transportation data sets that preserves privacy, (iii) fairness algorithms for transportation systems, and (iv) examining how GenAI systems are being integrated with transportation systems to provide security. Finally we discuss resiliency issues with respect to transportation systems where such systems and applications must continue to operate in the midst of attacks and failures.
Bio:
Dr. Bhavani Thuraisingham is the Founders Chair Professor of Computer Science and the Founding Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas (UTD). She is an elected Fellow of the ACM, IEEE, the AAAS, and the NAI. Her research interests are integrating cyber security and artificial intelligence/data science including as they relate to the cloud, social media, and Transportation Systems. She has received several technical, education and leadership awards including the IEEE CS 1997 Edward J. McCluskey Technical Achievement Award, the IEEE CS 2023 Taylor L. Booth Education Award, ACM SIGSAC 2010 Outstanding Contributions Award, the IEEE Comsoc Communications and Information Security 2019 Technical Recognition Award, the IEEE CS Services Computing 2017 Research Innovation Award, the ACM CODASPY 2017 Lasting Research Award, and the ACM SACMAT 10 Year Test of Time Awards for 2018 and 2019 (for papers published in 2008 and 2009). Her 44+ year career includes industry (Honeywell), federal research laboratory (MITRE), US government (NSF) and US Academia. Her work has resulted in 140+ journal articles, 300+ conference papers, 200+ keynote and featured addresses, seven US patents, sixteen books, and over 120 panel presentations including at Fortune Media, Lloyds of London Insurance, Dell Technologies World, United Nations, and the White House Office of Science and Technology Policy. She has also written opinion columns for popular venues such as the New York Times, Inc. Magazine, Womensday.com and the Legal 500, She received her PhD from the University of Wales, Swansea, UK, and the prestigious earned higher doctorate (D. Eng) from the University of Bristol, UK. She also has a Certificate in Public Policy Analysis from the London School of Economics and Political Science. She has been featured in the book by the ACM in 2024 titled: “Rendering History: The Women of ACM-W” as one of the 30+ “Women that Changed the Face of World Wide Computing Forever.”

Prof. Zhao Li
Zhejiang University & Hangzhou Yugu Tech, China
Title: A Graph Powered Large Scale Fraud Detection System
Abstract:
Graph-powered fraud detection is a common issue in various areas, such as e-commerce, banking, insurance and social networks, where data can be naturally formulated as graph structure. Especially in e-commerce, due to its large scale and enormous amount of real-time transactions over millions of merchandises, fraud detection has become an important and serious problem. The challenges lie in three aspects: sparse fraud samples, complex features in online transactions and extra large scale of e-commerce data. To deal with above issues, in this paper, we propose an efficient graph-powered large-scale fraud detection framework. Concretely, we first present a heterogeneous label propagation algorithm to recall more potentially fraudulent samples for further model training; then, we design a novel multi-view heterogeneous graph neural network model to obtain more accurate fraud predictions; finally, a fraud pattern analysis approach is presented to discover hidden fraud groups. In addition, in order to improve the efficiency and scalability of our proposed fraud detection framework, we present a large-scale fraud detection system deployed on a general graph computing engine. We conduct experiments on two real-world datasets. Results show that the proposed graph-powered fraud detection framework achieves high accuracy and superior scalability on large-scale graph data.
Bio:
Dr. Zhao Li obtained the Ph.D. from the University of Vermont (2012) and is currently serving as an adjunct professor at Zhejiang University. He has published over 60 top-tier papers (as first/corresponding author) , with multiple Best Paper Awards, including an IEEE-CCF honor. Former director of the Alibaba-ZJU Joint Research Center and chief scientist at TCL America. He is the distinguished member of CCF, and the Asia-Pacific AI Association. His accolades span the IEEE Open Source Science Award (China’s first), China Computer Federation Distinguished Speaker Award, UN ITU Innovate for Impact Award, and recognition among Stanford’s “Top 2% of Scientists.” His research interest lies in reinforcement learning, big-data-driven security, and large-scale graph computing.

Prof. Yonghao Wang
Birmingham City University, UK
Title: Blockchain as a Double-Edged Sword: Ensuring AI Model Traceability While Addressing Security and Fraud Risks
Abstract:
Blockchain technology, once hailed as a revolutionary force for trust and transparency, has increasingly been exploited for fraudulent activities, particularly in traditional applications such as NFTs and cryptocurrencies. The core issue lies in the absence of robust KYC mechanisms that can effectively map digital identities to real-world legal frameworks, allowing bad actors to manipulate these systems with little accountability. As AI-generated content and models become valuable assets, this problem intensifies—raising concerns over ownership verification, traceability, and ethical use. Without a secure method to authenticate and track AI models, issues such as deepfake proliferation, intellectual property theft, and misuse of generative AI models will escalate. This keynote will explore the dual nature of blockchain in AI security, examining both the risks and potential solutions. We introduce the ECHO Protocol (Encrypted Chain-based History of Ownership), a framework designed to establish clear ownership, version control, and responsible AI model licensing through blockchain, while integrating real-world legal accountability. By addressing the fundamental flaws in current blockchain applications, we propose a vision for a more secure, transparent, and legally aligned digital ecosystem.
Bio:
Dr. Yonghao (Leo) Wang is a Professor and Subject Lead for Cybersecurity at Birmingham City University, with over two decades of experience in digital signal processing, embedded systems, and secure communications. A seasoned academic and technology innovator, Dr. Wang has actively supported multiple start-ups across diverse innovation areas including networking, cybersecurity, blockchain, and artificial intelligence, driving applications in sectors ranging from multimedia to industrial automation. He has contributed to international standardisation efforts in digital audio and networking technologies.