LEONG Tze Yun

COM3-02-06

651 66519

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

Professor (Practice Track)

Ph.D. (Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA)
S.M. (Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA)
S.B. (Computer Science & Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA)

LEONG, Tze Yun is a Professor in the Department of Computer Science, School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT), U.S.A. Her research interests include responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. She is a Fellow of the American College of Medical Informatics (ACMI) and a founding Fellow of the International Academy of Health Sciences Informatics (IAHSI). With experience in both academia and industry, Tze Yun serves on program committees and editorial boards of leading conferences and journals. She also contributes to policy advisory and development in education, research and development, and ethics and governance in computer science, AI, and health informatics. She was a Board Member of the Health Sciences Authority (2020–2023) and an AI Advisor to the Urban Redevelopment Authority (URA) in Singapore. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan.

RESEARCH AREAS

Artificial Intelligence

RESEARCH INTERESTS

Cooperative Artificial Intelligence and Interactive Reinforcement Learning
Neurocognitive Modelling and Causal Reasoning
Responsible and General Artificial Intelligence
Biomedical and Health Informatics

RESEARCH PROJECTS

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

Ma H, Luo Z, Vo TV, Sima K, Leong TY. Highly efficient self-adaptive reward shaping for reinforcement learning. Proceedings of the The Thirteenth International Conference on Learning Representations (ICLR 2025); 24-28 April 2025; Singapore. 2025.
Ma H, Sima K, Vo TV, Fu D, Leong TY. Reward shaping for reinforcement learning with an assistant reward agent. Proceedings of the The Forty-first International Conference on Machine Learning (ICML 2024); 21-27 July 2024; Vienna, Austria. 2024.
Ma H, Vo TV, Leong T-Y. Mixed-initiative bayesian sub-goal optimization in hierarchical reinforcement learning. Proceedings of the The 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024); 6-10 May 2024; Auckland, New Zealand. 2024.
Yu K-H, Healey E, Leong T-Y, Kohane IS, Manrai AK. Medical artificial intelligence and human values. New England Journal of Medicine. 2024;390(20):1895-904. doi:10.1056/NEJMra2214183
Beam AL, Drazen JM, Kohane IS, Leong T-Y, Manrai AK, Rubin EJ. Artificial intelligence in medicine. New England Journal of Medicine. [Editorial]. 2023 March 30;388:1220-21. 10.1056/NEJMe2206291.
Vo TV, Bhattacharyya A, Lee Y, Leong T-Y. An adaptive kernel approach to federated learning of heterogeneous causal effects. In: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) 2022.
Vo TV, Lee Y, Hoang TN, Leong T-Y. Bayesian federated estimation of causal effects from observational data. In: James C, Kun Z, editors. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI); PMLR; 2022. p. 2024--34.
Vo TV, Wei P, Bergsma W, Leong TY. Causal modeling with stochastic confounders. In: Arindam B, Kenji F, editors. Proceedings of the The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 21); 13-15 April, 2021; PMLR; 2021. p. 3025--33.
Nguyen TT, Silander T, Li Z, Leong T-Y. Scalable transfer learning in heterogeneous, dynamic environments. Artificial Intelligence. Vol 247, June 2017, Pages 70-94.
Li Z, Narayan A, Leong T-Y. An efficient approach to model-based hierarchical reinforcement learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence AAAI-17; 4-9 Feb 2017; San Francisco, CA, USA. 2017.