Prof. Qing Li (IEEE Fellow)
The Hong Kong Polytechnic University, Hong Kong, China

Biography: Qing Li is a Chair Professor and Head of the Department of Computing, the Hong Kong Polytechnic University. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media, Web services, and e-learning systems. He has authored/co-authored over 500 publications in these areas, with over 74,500 citations and H-index of 108 (source: Google Scholars). He is actively involved in the research community, serving as an Editor-in-Chief of Computer & Education: X Reality (CEXR) by Elsevier; he has also served as an associate editor of IEEE Transactions on Artificial Intelligence (TAI), IEEE Transactions on Cognitive and Developmental Systems (TCDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data Science and Engineering (DSE), and World Wide Web (WWW) Journal, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits/sat in the Steering Committees of DASFAA, ACM RecSys, IEEE U-MEDIA, WISE and ICWL. Prof. Li is a Fellow of IEEE and IET/IEE.

Speech Title: Towards Multi-level Querying Methodology for an Indoor Robot Smart Space
Speech Abstract: Smart Space denotes dynamic, adaptive environments enhanced with robotics and AI technologies. Examples include smart homes/offices/cafes. By leveraging and integrating Computer Vision, Natural Language Processing, AIoT, Data Mining, Recommender Systems, and Sympathetic Computing, Smart Space can help improve efficiency, personalization, and user satisfactions with seamless interactions. In this talk, we introduce PolyRAG, a multi-level knowledge QA framework supporting multi-level querying for an indoor robot application system. Building on top of a naive RAG layer, we construct a knowledge pyramid by adding a knowledge graph layer and an ontology schema, so as to obtain a good balance of recall and precision when applied to a specific domain such as coffee robot interactions. We employ cross-layer augmentation techniques for comprehensive knowledge coverage and dynamic updates of the Ontology scheme and instances. To ensure compactness, we utilize cross-layer filtering methods for knowledge condensation in KGs. An experimental coffee robot prototype is constructed, and preliminary empirical studies are conducted to show the effectiveness of our PolyRAG supporting a waterfall model for querying from ontology to KG to chunk-based raw text.

Prof. Xiaoli Li (IEEE Fellow)

Singapore University of Technology and Design, Singapore

Biography: Professor Xiaoli Li is Chair Professor and Head of the Information Systems Technology and Design (ISTD) Pillar at the Singapore University of Technology and Design (SUTD). Prior to joining SUTD, he headed the Machine Intellection Department at A*STAR, where he established and led Singapore’s largest AI research group consisting of more than 130 AI scientists. He is also an Adjunct Full Professor at Nanyang Technological University (NTU) and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Asia-Pacific Artificial Intelligence Association (AAIA), and PHM Society.
Professor Li is an internationally recognised researcher in artificial intelligence, machine learning, data mining, and bioinformatics, with a strong emphasis on translating AI research into practical applications. He has published over 400 peer-reviewed papers, received more than 40,000 citations, and achieved an h-index of 96. His research contributions have earned more than ten best paper awards, including the prestigious DASFAA 10+ Year Best Paper Award. He currently serves as an Associate Editor of IEEE Transactions on Artificial Intelligence and ACM Computing Surveys.
Beyond his academic achievements, Professor Li has extensive experience in leading large-scale, industry-driven AI research and innovation. He has established and directed several joint research laboratories and spearheaded strategic R&D collaborations with leading international organisations across aerospace, telecommunications, insurance, professional services, and other industries. His work bridges fundamental AI research and real-world deployment, advancing intelligent solutions for complex industrial and societal challenges.
His contributions have received international recognition, including being ranked among the world's top 2% of scientists and being named a Clarivate Highly Cited Researcher.
For more information, please visit Professor Xiaoli Li's SUTD profile.

Speech Title:  Advancing Design AI: From Industrial Sensor Intelligence to Robotics
Speech Abstract: Artificial intelligence (AI) delivers transformative value when it moves beyond algorithmic advances to address complex real-world challenges. Achieving this requires intelligent systems that can learn from heterogeneous data, support human decision-making, adapt to dynamic environments, and operate reliably under practical constraints. This keynote presents an integrated perspective on Design AI, highlighting how advances in machine learning can be translated into intelligent, trustworthy, and deployable solutions across education, industry, and robotics.
The keynote begins with AI-enabled education, exploring how intelligent techniques can enhance programming education through automated assessment, personalised feedback, and adaptive learning support. By analysing students’ code and programming behaviours, AI systems can identify misconceptions, diagnose learning difficulties, and provide timely, actionable feedback, while enabling instructors to support larger and more diverse student cohorts. The rapid emergence of generative AI introduces new opportunities and challenges, particularly in assessing authentic learning, distinguishing human-written from AI-generated code, and rethinking assessment strategies in AI-assisted educational environments.
The second part focuses on AI-driven industrial intelligence, where modern machines and cyber-physical systems continuously generate large volumes of complex, multivariate sensor data. Learning robust and informative representations from these often noisy, heterogeneous, and sparsely labelled time-series data is essential for applications such as anomaly detection, equipment health monitoring, remaining useful life prediction, and predictive maintenance. Drawing on selected industrial collaborations and deployments, the keynote illustrates how AI can transform raw sensor streams into actionable intelligence, improving operational reliability, reducing unplanned downtime, and enabling timely decision-making. Particular attention is given to developing efficient and scalable AI solutions that can operate in real time, including on resource-constrained edge devices.
The final part extends these foundations to embodied intelligence and robotics, where AI must move beyond interpreting data to perceiving, reasoning, adapting, and acting in the physical world. Integrating visual perception, temporal sensor information, environmental understanding, and physical interaction introduces new challenges in robustness, autonomy, and adaptability. Research examples spanning underwater mapping, autonomous ship-hull inspection, intelligent navigation, and reconfigurable robotics demonstrate how AI-enabled robotic systems can operate effectively in complex, uncertain, and dynamic environments.
By connecting these three domains through a unified Design AI perspective, the keynote emphasises the importance of integrating data-driven learning, and real-world deployment. The central message is that the future of impactful AI lies not merely in developing increasingly powerful models, but in designing intelligent systems that are reliable, adaptive, trustworthy, and capable of delivering tangible benefits to people, industries, and society.

Invited Speakers