Keynote Speakers
Distinguished researchers sharing their insights and vision
Confirmed Speakers
Meet the distinguished researchers delivering keynote addresses at KSS 2026.
Prof. Mengjie Zhang
Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, New Zealand
“Evolutionary Machine Learning: 70 Years of Progress”
Abstract. Evolutionary machine learning has been very popular over the recent years. In this talk, I will firstly provide a brief overview of the history of evolutionary machine learning with the major developments over the past 70 years, then visit the main paradigms of evolutionary machine learning and their successes in classification, feature selection, regression, clustering, computer vision and image analysis, scheduling and combinatorial optimisation, deep learning, transfer learning and XAI/XML, and generative AI. The main applications, challenges and potential opportunities will be also discussed.
Biography. Mengjie Zhang is a Fellow of the Royal Society of New Zealand, a Fellow of Engineering New Zealand, a Fellow of the IEEE, an IEEE Distinguished Lecturer, and Professor of Computer Science (Artificial Intelligence) at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation and Machine Learning Research Group. He is also the Director of the Centre for Data Science and Artificial Intelligence at the University. His research is mainly focused on AI, machine learning and big data. He received the “Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe 2023”, the “2024 Australasian Artificial Intelligence Distinguished Research Contribution Award”, and the ACM SIGEVO Outstanding Contribution Award in 2025. He is also a Clarivate Highly Cited Researcher in 2023–2025. Since 2007, he has been listed as a top five (currently No. 2) world genetic programming researcher by the GP bibliography. Prof Zhang is a member of the IEEE CIS Nomination Committee, and the immediate past Chair for the IEEE CIS Awards Committee. He is also a past Chair of the IEEE CIS Intelligent Systems Applications Technical Committee, the Emergent Technologies Technical Committee and the Evolutionary Computation Technical Committee, a past Chair for the IEEE CIS PubsCom Strategic Planning subcommittee, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
Prof. Byeong Ho Kang
School of Information and Communication Technology, University of Tasmania, Hobart, Australia
“Trustworthy Orchestration AI: From Ethical Principles to Governed Agentic Systems”
Abstract. Generative and agentic AI systems are changing how decisions are made, but they are being adopted faster than they can be properly governed. This creates serious risks involving opacity, accountability, autonomy, and ethical misalignment. This presentation argues that trustworthy AI cannot rely only on ethical guidelines or after-the-fact compliance checks. Trust has to be built into the system architecture from the beginning. The presentation introduces Trustworthy Orchestration AI as a framework for designing governed agentic AI systems. Rather than treating AI as a single autonomous controller, the framework views AI as a coordinated set of specialised modules operating under explicit policies, symbolic gatekeeping, human oversight, and auditable accountability mechanisms. I will outline thirteen architectural criteria, including the separation of governance and execution, symbolic mediation, human-in-the-loop oversight, auditability, lifecycle governance, resilience, and semantic communication integrity. Together, these criteria show how ethical and responsible AI principles can be translated into practical design rules for safer, more accountable, and more trustworthy AI systems.
Biography. Professor Byeong Ho Kang is an internationally recognised researcher in artificial intelligence, information systems, and knowledge engineering. He is known for pioneering hybrid, human-centred AI frameworks that integrate symbolic reasoning, machine learning, and governance mechanisms. His “orchestration paradigm” separates governance from execution, embedding ethics and accountability by design. This vision has been applied in projects spanning health platforms, smart factory systems, and broader industrial applications. His research extends well beyond academia through collaborations with the US Air Force, US Navy, Lockheed Martin Australia, CSIRO, Hyundai Steel, and SeeGen in South Korea. His applied AI systems cover intrusion detection, conversational robotics, smart data systems, industrial optimisation, and medical decision support. Over his career, he has published more than 300 papers with over 6,000 citations and led more than 25 large-scale projects across healthcare, defence, ICT, agriculture, and the smart factory industry. Professor Kang’s work on AI and human intelligence shows how emerging technologies act as both disruptor and enabler. In addition to his research, Professor Kang has held major leadership roles, including Head of the School of ICT at the University of Tasmania (2000–2024) and President of the Korean Academy of Scientists and Engineers in Australasia (2016–2017). He has also served as chair and committee member for leading international conferences such as the Pacific Rim International Conference on Artificial Intelligence and the Australian AI Conference.
Prof. Van-Nam Huynh
School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST), Japan
“A Unified Framework for Reliable Multi-label Classification”
Abstract. Multi-label classification (MLC) is widely used in applications such as medical diagnosis, where several labels may apply to the same case. In high-stakes settings, however, accurate predictions alone are not enough: a reliable system should also know what objective it is optimizing, express uncertainty when the evidence is insufficient, and recognize inputs that differ from those seen during training. This talk explores reliable multi-label classification through three fundamental questions: Are predictions aligned with what we actually want to measure? Can a model acknowledge uncertainty instead of always making a definite decision? And can it recognize when it encounters something unfamiliar? We present principled approaches to address each of these questions by aligning predictions with evaluation criteria, allowing uncertain label decisions to remain unresolved, and separating label prediction from the detection of novel inputs. Together, these ideas offer a broader view of reliability in multi-label learning and provide practical tools for building systems that are more trustworthy, transparent, and robust. Their effectiveness is illustrated on chest radiographs and standard multi-label benchmark datasets.
Biography. Prof. Van-Nam Huynh is a Professor at the School of Knowledge Science, JAIST, with research interests spanning decision analysis and management science, data mining and machine learning, modelling and reasoning with uncertain knowledge, multi-agent systems, and kansei information processing. He has authored over 270 publications and is an active member of the international knowledge science community.
Prof. Yu-Wang Chen
Centre for AI and Decision Sciences, Alliance Manchester Business School, The University of Manchester, United Kingdom
“Decision Intelligence: Integrating Data, Knowledge, and AI”
Abstract. With the rapid advancement of artificial intelligence (AI) and data-driven technologies, Decision Intelligence is becoming increasingly important for supporting effective decision-making in complex, uncertain and dynamic environments. While AI and data-driven approaches provide new opportunities to analyse complex data, identify patterns and generate actionable insights, effective decision-making also requires the integration of human knowledge, heterogeneous sources of evidence and uncertainty. This talk will explore recent advances in Decision Intelligence and AI-enabled decision modelling, with a particular focus on methodologies that integrate evidential reasoning, machine learning and data analytics. It will discuss how knowledge, data and AI can potentially be brought together to support robust and trustworthy decision-making and showcase real-world applications across different domains.
Biography. Yu-Wang Chen is Professor of Decision Sciences and Business Analytics at Alliance Manchester Business School, The University of Manchester, where he also serves as Co-Director of the Centre for AI and Decision Sciences. He was a Turing Fellow at The Alan Turing Institute from 2021 to 2023. Prior to joining the University of Manchester, he was a Postdoctoral Research Fellow in the Department of Computer Science at Hong Kong Baptist University. He received his PhD in Systems Engineering from Shanghai Jiao Tong University. His research focuses on Decision Sciences, Data Analytics and AI, with applications across business analytics, risk analysis and healthcare decision support. He has published more than 100 research articles in leading international journals and conference proceedings. He has led and contributed to a portfolio of research and industry projects as Principal Investigator and Co-Investigator, funded by organisations including Innovate UK, the Turing–UoM Fund, EPSRC and ERDF. He serves as an Associate Editor and Editorial Board Member for several international journals.