651 66519


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 a Professor of Practice of Computer Science at the School of Computing, National University of Singapore (NUS). She directs the Medical Computing Laboratory at the School. She is also the Director of the NUS Artificial Intelligence Laboratory (NUSAiL). Tze Yun received her SB, SM, and PhD degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT), USA. Her research interests include responsible and decision-theoretic AI, neurocognitive modelling and causal reasoning, transfer and reinforcement learning, human-aware and adaptive computing, artificial general intelligence, and biomedical and health informatics. She has published and served on editorial boards and programme committees of leading international journals and conferences in AI and biomedical informatics. She is currently a Guest Editor of the Artificial Intelligence in Medicine series for the New England Journal of Medicine. 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 both academic background and business experience, Tze Yun regularly contributes to panels and committees on education, research and development, and ethics and governance strategies and policies in Computer Science, Artificial Intelligence and Health Informatics in Singapore and abroad. She is currently serving in AI advisory roles for the Urban Redevelopment Authority (URA) in Singapore, the Advisory Council on AI in Uzbekistan, the AI in Health Ethics and Governance guidance initiatives of the World Health Organization (WHO), and the AI Governance Alliance of the World Economic Forum (WEF).



  • 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





  • 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, Hoang TN, Leong T-Y. Adaptive multi-source causal inference from observational data. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM) 2022. p. 1975-85.
  • 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.
  • Wei PF, Leong TY. Randomized transferable machine. In: Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR); 10-15 Jan. 2021. p. 8711-18.
  • 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.
  • Pillai PS, Leong TY, Alzheimer’s Disease Neuroimaging Initiative. Fusing heterogeneous data for Alzheimer's disease classification.Stud Health Technol Inform. 2015;216:731-5


  • Founding Fellow, International Academy of Health Sciences Informatics (IAHSI)

  • Fellow (International), American College of Medical Informatics (ACMI)

  • Member, Eta Kappa Nu (Honor Society for Electrical Engineers)


Foundations of Artificial Intelligence


In the News

13 February 2019


21 May 2021
In 1961, something momentous happened at a squat, nondescript factory in the tiny town of Ewing, New Jersey. The Unimate, ...