LEE Wee Sun

LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT.

Education:

• Ph.D., Australian National University, Canberra, Australia (1996)
• B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992)

Research Focus: Professor Lee pioneers work in Machine Learning, Planning Under Uncertainty, and Approximate Inference, with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures).

Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems.

Awards:

• IJCAI-JAIR Best Paper Prize (2022)
• RSS Test of Time Award (2021)
• RoboCup Best Paper Award (IROS 2015)
• HRATC 1st Place (2015)
• IPPC POMDP Track 1st Place (2011, 2014)
• UAI Google Best Student Paper (2014)
• Semeval-1 1st/2nd Place (2007)
• J.G. Crawford Prize (ANU 1996)

Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring.

Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.