Biography: Professor Irwin King is a globally recognized scholar in the field of machine intelligence, currently serving as the Pro-Vice-Chancellor (Education) and Professor at the Department of Computer Science & Engineering at The Chinese University of Hong Kong. His extensive research interests encompass a wide range of areas, including trustworthy AI, machine learning, social computing, AI, and data mining. Professor King is a Fellow of esteemed societies and associations, such as the ACM, IEEE, AAAI, and INNS. Throughout his career, he has assumed various leadership roles in numerous prominent conferences and societies. Notably, he held the position of President of the International Neural Network Society, General Co-chair for conferences such as WebConf 2020, ICONIP 2020, ACML 2015, RecSys 2013, and WSDM 2011. Additionally, he has held leadership capacities in conferences such as WWW, NIPS, ICML, IJCAI, AAAI, and ICONIP. Presently, Professor King continues to serve as the Vice-President of the ACM SIGWEB, the Vice-President of the WebConf Steering Committee, and a board member of the International Neural Network Society (INNS) and Asia Pacific Neural Network Society (APNNS).
Professor King has received numerous prestigious awards for his contributions to the field of machine intelligence. Notable accolades include the 2021 INNS Dennis Gabor Award for engineering applications of neural networks, the 2020 APNNS Outstanding Achievement Award, and several Test of Time Awards from ACM conferences such as CIKM2019, SIGIR 2020, and WSDM 2022. During his sabbatical leave at AT&T Labs Research in San Francisco, he was a Visiting Professor and taught classes at UC Berkeley.
Presently, Professor King is the Director of the Centre for Learning Innovation and Technology (ELITE), dedicated to promoting eLearning through education technology. Additionally, he oversees the Machine Intelligence and Social Computing (MISC) Lab, which conducts research in machine learning-related fields.
Professor King holds a Bachelor of Science degree in Engineering and Applied Science from the California Institute of Technology (Caltech) and obtained his Master of Science and Doctor of Philosophy degrees in Computer Science from the University of Southern California (USC).
Title of Speech: Trustworthy Agentic AI in the Era of Large Language Models
Abstract: Large language models are rapidly evolving from static chatbots into agentic AI systems that can plan, use tools, access memory, interact with external environments, and operate over long-horizon workflows. These systems are enabling powerful applications in software engineering, healthcare, finance, intelligent assistants, scientific discovery, and enterprise automation. However, their growing autonomy also introduces new trustworthiness challenges: failures may emerge not only from incorrect final answers, but also from unsafe tool use, goal drift, indirect prompt injection, privacy leakage, insecure memory, and cascading errors across multi-step trajectories.
This talk presents our recent work on trustworthy agentic AI, focusing on safety, robustness, privacy, and system security for large language model agents. The talk will first trace the evolution from trustworthy AI and large language models to modern agentic systems, and briefly review the current state of the art in retrieval-augmented agents, tool-using agents, long-context agents, and multi-agent systems. It will then organize key risks and mitigation strategies along the agent lifecycle of Perceive, Plan, Act, Reflect, and Learn, and discuss how outcome-level and process-level evaluation can support trustworthy deployment.
Together, these perspectives show that trustworthy agentic AI is a system-level challenge involving models, tools, memory, environments, evaluation, governance, and human oversight. The talk will conclude with applications and open challenges for building verifiable, privacy-preserving, and accountable agentic AI systems.
Biography: Prof. Yingxu Wang, FIEEE, FBCS, FI2CICC, FAAIA, FNYA, FWIF, P.Eng.; Professor emeritus, Dept. of ECE and Hotchkiss Brain Institute, Univ. of Calgary, Canada; Distinguished Chair Prof., Dean of Chongqing Research Institute of Intelligent Mathematics (IM) and Autonomous Intelligence (AI*), RI-IM.AI*; President of Int'l Institute of Cognitive Informatics and Cognitive Computing (I2CICC); Visiting Professors on sabbaticals at Stanford Univ. (2008|16), MIT (2012), UC Berkeley (2008), Oxford Univ. (1995|2018-23). Distinguished Visiting Professor at Tsinghua Univ. (2019-2022). Academic Committee Member of Tsinghua Beijing National Center of Information Science and Technology.
He received a PhD in Computer Science from Nottingham Trent University, UK in 1998, and has been a full professor since 1994. He is a distinguished chair professor of intelligent mathematics, intelligence science, brain science, and software science, Dean and Chief Scientist of CQRI-IM.AI*, and Honorary Chair of the University Academic Committee, Chongqing Univ. of Science and Technology (CQUST).
He has contributed to fundamental and transdisciplinary knowledge of AI*/IS/IM by: a) Formally proofing 100+ theorems in IM and IS; and b) Presenting 100+ invited keynote speeches in international conferences.
Title of Speech: On the Emergence of Autonomous AI (AI*): from Empirical Big-Data Engineering to Rigorous Intelligent Science
Abstract: This keynote presents the theoretical foundations of contemporary Intelligent Science (IS) underpinned by fundamental studies in Intelligent Mathematics (IM). It queries how empirical AI technologies will be matured towards IS driven by IM, that leads to the emergence of Autonomous AI (AI*) [6]. Advances in AI* underpinned by IM have revolutionarily transferred AI technologies from empirical training-based big-data engineering to contemporary IS embodied by IM-based machine intelligence generation [7]. The AI* technology works autonomously without the need for pervasive empirical training, because rational proofs about the natural and the mental worlds can’t be merely derived by case-sensitive data or empirical instances, constrained by their unlimited domains and real-time constraints. The formal and fundamental studies on IS and IM have led to the revealment of the first axiom of IS and AI, i.e., “The theoretical foundation of AI is IS, while that of IS is IM, shortly: AI_IS_IM.
On the basis of the preceding causality, this keynote presents a scientific framework of IS underpinned by IM. The rigorous approach has provided a formal methodology for AI* system design and implementation. AI* and IM will theoretically and empirically improve traditional AI technologies, which had not only been constrained by unbounded data scales and exponential complexity, but also unreasonable training energy consumption.
Biography: Kang Li received the Ph.D. degree in Mechanical Engineering from University of Illinois at Urbana Champaign, Champaign, IL, USA, in 2009. He is now a full professor of the Biomedical Big Center at West China Hospital and Associate Dean for Research at Sichuan University Pittsburgh Institute (SCUPI). Before joining West China Hospital, He was an associate professor with the Department of Orthopaedics, Rutgers New Jersey Medical School (NJMS) and an assistant professor with Department of Industrial and Systems Engineering, Rutgers University. He was also a graduate faculty member in the Departments of Biomedical Engineering and Computer Science at Rutgers University. He serves as an associate editor of IEEE Transactions on Human-Machine Systems. His research interests include AI in healthcare, healthcare systems engineering, medical imaging, biomechanics, biorobotics, and human factors/ergonomics.
Title of Speech: Artificial Intelligence in Medicine: Our Journey at West China Hospital
Abstract: The integration of Artificial Intelligence into healthcare demands more than theoretical breakthroughs; it requires robust validation within the chaotic, high-stakes environments of real-world clinical practice. This talk outlines our comprehensive journey at West China Hospital, illustrating how we bridge the critical gap between algorithmic innovation and frontline medical reality. We will explore our multidisciplinary milestones across three foundational pillars of intelligent medicine: System-Level Clinical Optimization， Diagnostic Intelligence & Imaging， Embodied AI & Surgical Robotics. Ultimately, this presentation serves as a blueprint for translating deep technological capabilities into systemic hospital transformation, redefining human-machine collaboration, and delivering next-generation care to the patients who need it most.
Copyright © 2026 the 9th International Conference on Big Data and Artificial Intelligence (BDAI 2026). All rights reserved.
