LEONG Tze Yun

COM3-02-06

651 66519

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

Professor (Practice Track)
Director, NUS AI Laboratory

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 Professor of Computer Science (Practice Track) at the School of Computing, National University of Singapore (NUS). Tze Yun received her SB, SM, and PhD degrees in Computer Science from Massachusetts Institute of Technology (MIT), USA. Her research interests include responsible AI, dynamic decision making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. She is an elected Fellow of the American College of Medical Informatics (ACMI) and a founding Fellow of the International Academy of Health Sciences Informatics (IAHSI). With a background in academia and business, Tze Yun participates in program committees and editorial boards of leading academic conferences and journals. She also contributes to policy work in education, research and development, ethics and governance strategies in Computer Science, Artificial Intelligence (AI), and Health Informatics. Previously, she served as a Board Member of Health Sciences Authority in Singapore (2020-2023) and Director of the former NUS Artificial Intelligence Laboratory (2022-2024). Currently, she is an AI advisor at the Urban Redevelopment Authority (URA) in Singapore and Advisory Council on AI in Uzbekistan. She is also a member of World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health and World Economic Forum's AI Governance Alliance.

RESEARCH AREAS

Artificial Intelligence

RESEARCH INTERESTS

Responsible and Decision-theoretic Artificial Intelligence
Mixed Initiative Machine Learning and Decision Making
Neurocognitive Modelling and Causal Reasoning
Transfer and Reinforcement Learning
Biomedical and Health Informatics

RESEARCH PROJECTS

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

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.
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.
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.
Koch S, Hersh WR, Bellazzi R, Leong TY, Yedaly M, Al-Shorbaji N. Digital health during covid-19: Informatics dialogue with the world health organization. Yearb Med Inform. 2021 Apr 21. 10.1055/s-0041-1726480.
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.