I am Group Lead of AI for Transdisciplinary Science (AI4TS) and a Senior Research Scientist at CFAR, A*STAR, Singapore. I received my Ph.D. degree from The Hong Kong Polytechnic University in 2017. My research develops structure-aware and data-efficient learning methods for graph intelligence, foundation models, and AI-enabled scientific discovery.
My research develops structure-aware, data-efficient, and scientifically grounded learning methods for foundation models, graph intelligence, and AI-enabled scientific discovery.
Long-term research themes that define my broader research agenda.
I develop learning methods that incorporate relational, geometric, and structural priors into modern AI systems, with applications in graph learning, sparse computation, and structural attention.
I study data-efficient and structure-aware approaches for adapting foundation models, with interests in federated learning, multimodal reasoning, and scalable representation learning.
I apply machine learning and foundation models to scientific problems in materials, catalysis, biology, weather modelling, and environmental systems.
Current projects that connect methodological research with high-impact applications.
This project develops efficient foundation models and agentic AI workflows for accelerating discovery in surface science and catalysis, integrating multi-fidelity data, sparse learning, inverse design, and experimental validation.
FLASH develops spatiotemporal and multimodal AI methods for dynamic, zone-based lightning risk forecasting and decision support for airport operations.
I have had the privilege of working with the following talented researchers and students in my group.
ICLR 2027 (Area Chair); ICASSP 2026 (Area Chair); IJCNN 2025-2027 (Area Chair); EITCE 2025 (Publicity Chair); and ISMIS 2018 (Session Chair).
ICML 2025 and 2026; ICLR 2025 and 2026; NeurIPS 2024-2026; AISTATS 2025 and 2026; AAAI 2026 and 2027; and BIBM 2023–2026.
Associate Editor, Memetic Computing, 2025–Present.
Action Editor, Transactions on Machine Learning Research, 2026–Present.
AIJ IEEE TPAMI IEEE TKDE IEEE TCYB IEEE TNNLS IEEE TSMC IEEE TFS IEEE/CAA JAS IEEE TSC IEEE TCBB IEEE TETCI IEEE TNSE IEEE TBD IEEE TCSS ACM TKDD KAIS Bioinformatics
Two research positions (Scientist and Senior Scientist) are available in my group at CFAR, ASTAR. These positions focus on frontier AI methods for scientific discovery and environmental intelligence. Interested candidates are encouraged to apply through the official ASTAR Careers links below.
Lead advanced AI research for scientific discovery, with applications in materials, surface science, and catalysis. Research directions include geometric and equivariant learning, graph and foundation models, generative AI, multi-fidelity learning, and efficient AI.
Develop spatiotemporal, multimodal, and structure-aware AI for weather forecasting and environmental hazard prediction, including lightning nowcasting. Topics include sequence and video prediction, multimodal weather-data fusion, graph learning, efficient attention, and uncertainty-aware forecasting.
I welcome Ph.D. students and researchers for short- or long-term visits, including visits supported by CSC or other institutional funding schemes.
Prospective visitors may contact me by email with a CV, a brief research statement, and information about the intended funding programme or visit arrangement.