CHUA Tat Seng

AS6-05-08

651 62505

www.comp.nus.edu.sg/~chuats

Professor
Director, NUS-Tsinghua Extreme Search Center (NExT)

Ph.D. (Computer Science, University of Leeds, UK, Feb 1983)
B.Sc. (Civil Engineering & Computer Science, University of Leeds, UK, Jun 1979)

Dr. Chua Tat Seng is the KITHCT Chair Professor at the School of Computing, National University of Singapore (NUS). He is also the Distinguished Visiting Professor of Tsinghua University, Sichuan University and Zhengzhou University. Dr. Chua was the Founding Dean of the School of Computing from 1998-2000. His research interests include multimodal foundation models, responsible AI, and conversational search and recommendation. He is the co-Director of NExT, a joint research Center between NUS and Tsinghua University. Dr Chua is the recipient of 2015 ACM SIGMM Achievements Award, 2022 NUS Research Recognition Award, 2024 CCF Overseas Outstanding Technical Contributions Award, and the Fellow of the Singapore National Academy of Science. He is the Chair of the steering committee of Multimedia Modeling (MMM) conference series, and ACM International Conference on Multimedia Retrieval (ICMR) (2015-2018). He was the General Co-Chair of ACM Multimedia 2005, ACM SIGIR 2008, ACM Web Science 2015, WSDM 2023, ACM Web Conference (or WWW) 2024, and the fourth coming ACM ICAIF 2025. His group has won many best paper awards, including the recent Outstanding Paper Award at ICLR 2025. Dr. Chua is the co-Founder of two technology startup companies in Singapore.

RESEARCH AREAS

Media

RESEARCH INTERESTS

Unstructured Multimodal Data Analytics
Multimodal Foundation Model
Responsible AI: Trust and Safety of AI
Conversational Search and Recommendation

RESEARCH PROJECTS

2025-2028: Trustworthy Multimodal Foundation Models: A Scalable Multi-Agent Approach. Agency: NRF-IMDA, Singapore. Grant S$4.9M.

Artificial Intelligence

2024-25: Enhancing Financial Investment Decision-making with Retrieval-augmented LLM. Agency: AIDF, Singapore. Grant: S$500K

Artificial Intelligence

2021-23: Enhancing Financial Investment Decision-making with Retrieval-augmented Large Language Models. Agency: AIDF, Singapore. Grant: S$888K.

Artificial Intelligence

2021-23: Learning & Reasoning on Knowledge Graph-Enhanced Info Retrieval. Agency: DSTA. Grant: S$750K

Media

2021-23: Sea-NExT Joint Lab. Company: Sea Group. Grant: S$2M

Artificial Intelligence

2015-18: NExT++: Towards Web Intelligence and User Empowerment. Agency: NRF Singapore. Grant: S$12M.

Media

RESEARCH GROUPS

NExT++

Media

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

J Fang, H Jiang, K Wang, Y Ma, S Jie, X Wang, X He & T-S Chua. AlphaEdit: Null-space constrained knowledge editing for language models. ICLR 2025 (Best Paper Award).
T Yu, Y Yao, H Zhang, T He, Y Han, G Cui, J Hu, Z Liu, H-T Zheng, M Sun & T-S Chua. RLHF-V: Towards trustworthy MLLMs via behavior alignment from fine-grained correctional human feedback. CVPR 2024,
S Wu, H Fei, L Qu, W Ji & T-S Chua. Next-GPT: Any-to-Any Multimodal LLM. ICML 2024.
X Wang, X He, M Wang, F Feng & T-S Chua. Neural Graph Collaborative Filtering. SIGIR 2019.
X Wang, X He, Y Cao, M Liu & T-S Chua. KGAT: knowledge graph attention network for recommendation. KDD 2019.
Q Sun, Y Liu, T-S Chua & Bernt Schiele. Meta-transfer learning for few-shot learning. CVPR 2019.
F Feng, X He, X Wang, C Luo, Y Liu & T-S Chua. Temporal relational ranking for stock prediction. ACM TOIS 2019.
L Chen, H Zhang, J Xiao, L Nie, J Shao, W Liu & T-S Chua. SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning. CVPR 2017.
X He, L Liao, H Zhang, L Nie, X Hu & T-S Chua. Neural collaborative filtering. WWW 2017.
J Chen, H Zhang, X He, L Nie, W Liu & T-S Chua. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. ACM SIGIR 2017.
H Zhang, Z Kyaw, S-F Chang & T-S Chua. Visual translation embedding network for visual relation detection. CVPR 2017.
J Chen, H Zhang, X He, L Nie, W Liu & T-S Chua. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. ACM SIGIR 2017.