CMILT Chartered Member, MLogM
Universiti Sains Malaysia

Areas of Expertise: Data Mining, Machine Learning, Artificial Intelligence, Business Analytics, Statistical Process/Quality Control, Robust Statistics, TRIZ, etc.

Steering Committee, Young Scientists Network-Academy of Sciences Malaysia (YSN-ASM).
Department of Data Science and Artificial Intelligence, School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Malaysia

Areas of Expertise: User Experience (UX)/ User Interface (UI), Human-Computer Interaction (HCI)/ Interaction Design, Product & Service Design, Software Engineering & Mobile Development, Information Visualization & Analytics, Multimedia, Information and Communication Technology, Information System, Software Engineering and Entre/Technopreneurship
Speech Title: From Interface to Intelligence: Designing the Future of Work, Care, and Connection through AI+HCI
Abstract: As artificial intelligence (AI) evolves from a passive tool to an active collaborator, and human-computer interaction (HCI) shifts from screen-based interfaces to immersive, context-aware experiences, the convergence of AI and HCI, AI+HCI has become a pivotal force reshaping industries and society. However, a persistent challenge remains: the disconnect between advanced AI capabilities and real-world usability. Despite breakthroughs in machine learning, explainable AI (XAI), and edge computing, many industrial deployments fall short due to poor user integration, lack of trust, ethical concerns, and failure to adapt to diverse user needs and complex environments. This gap risks creating intelligent systems that are technically powerful yet practically unusable, inaccessible, or even harmful. This keynote addresses that gap, framing AI+HCI not just as a technological solution, but as a human-centered design imperative. Through real-world case studies from healthcare diagnostics and collaborative robotics to adaptive learning and productivity systems, this talk explores how multimodal interaction, Edge-AI integration, cobots, and inclusive user requirement analysis can be strategically designed to solve critical societal and industrial challenges. The keynote urges a shift in mindset: from usability to trustability, from automation to collaboration, and from smart systems to empathetic systems. It calls upon researchers, technologists, designers, and industry leaders to co-create ecosystems where AI+HCI truly enhances work, care, and human connection, safely, ethically, and sustainably.

School of Computer Science and Engineering

Areas of Expertise: New computing models, quantum computing, distributed quantum algorithms, quantum models learning, quantum communication, fuzzy and probabilistic as well as quantum discrete event systems
Speech Title: Quantum Model Learning: Learning quantum finite automata via queries
Abstract: Learning finite automata (termed as model learning) has already become an important field in machine learning with useful realistic applications. One-way quantum finite automata (1QFA) are simple models of quantum computers with finite memory, and have advantages over traditional finite automata with regard to state complexity. Also, recently, Qiu has applied 1QFA to discrete event systems and established a basic framework of quantum discrete event systems. Due to their simplicity, 1QFA have well physical realizability. As a different problem and new topic in quantum learning theory and quantum machine learning, in this talk, we would introduce learning 1QFA with queries (naturally it is termed as quantum model learning), including: (1) A learning algorithm for measure-once one-way QFA (MO-1QFA) with query complexity of polynomial time; (2) A learning algorithm for measure-many one-way QFA (MM-1QFA) with query complexity of polynomial-time, as well. Also, we mention potential methods for studying other 1QFA in this framework.

Jinan University, Guangzhou, China
Provincial Young Talents, Editorial Board Member of IEEE Transactions on Industrial Electronics and Neural Processing Letters, IEEE Senior Member

Areas of Expertise: Computational Intelligence, Robotics, Distributed Optimization and Control
Speech Title: Advances in Design and Application of Distributed k-Winners-Take-All Models
Abstract: The k-winners-take-all (KWTA) is an important competition mechanism that is of great significance in nature and social phenomena. In the past two decades, lots of KWTA models have been developed from different academic societies, including signal processing, circuits and systems, neural networks, and multi-agent systems. To deal with the robustness and scalability issues of centralized KWTA models, distributed KWTA models have been proposed in recent years. In this talk, the recent advances in the design and applications of distributed KWTA models will be discussed. The potential research topics for KWTA will also be mentioned.

Universität Bremen, Germany, Nanjing University of Information Science & Technology, China

Areas of Expertise: Biosignal processing, human activity recognition, virtual reality, and music information retrieval
Speech Title: Human-Triggered Machine Learning (HTML)
Abstract: The ultimate goal of AI is to serve humanity. We should avoid pursuing AI for its own sake, and instead focus on the human factors that permeates every stage of AI development, training, and application. For instance, much of the research in multimodal physiological signals stems from real human needs, leveraging advances in both hardware and software to achieve effective and efficient AI integration. How to distill these human requirements and integrate them with AI has become a critical challenge—often taking precedence over optimizing an already good model.

Northwestern Polytechnical University

Areas of Expertise: Bionic and Intelligent Robotics, Biomechatronics, Human Factors and Intelligence
Speech Title: To discover perception and cognitive state: sequence-based and graph-based analysis method of eye movement
Abstract: Eye movement can show the cognitive process in performing tasks to a certain extent and play an important role in the HCI design and evaluation. The existing researches on eye movement analysis are usually based on statistics method, and it is difficult to show
the correlation between the information associated with the scene. Other probabilistic algorithms usually focus on user feature recognition based on eye movement representation. In this talk, the concept of time-domain and frequency-domain analysis of eye movement area of interest is proposed, within which, the frequent pattern mining method and visual cognitive graph model are constructed to mine the relationship between the areas of interests. Finally, some application examples of this model in the cognitive state evaluation and novice- expert experience extraction are presented.