LEE Wee SunProfessor
Vice Dean, Research
Ph.D. (Australian National University, Canberra, Australia, 1996)
B.Eng. (Computer Systems Engineering, Australian National University, Brisbane, Australia, 1992)
- Artificial Intelligence
- Machine Learning
- Planning Under Uncertainty
- Approximate Inference
Lee Wee Sun is a professor in the Department of Computer Science, National University of Singapore. He obtained his B.Eng from the University of Queensland in 1992 and his Ph.D. from the Australian National University in 1996. He has been a research fellow at the Australian Defence Force Academy, a fellow of the Singapore-MIT Alliance, and a visiting scientist at MIT. His research interests include machine learning, planning under uncertainty, and approximate inference. He has been an area chair for machine learning and AI conferences such as the Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), the AAAI Conference on Artificial Intelligence (AAAI), and the International Joint Conference on Artificial Intelligence (IJCAI). He was a program, conference and journal track co-chair for the Asian Conference on Machine Learning (ACML), and he is currently the co-chair of the steering committee of ACML.
- Approximate Inference using Low Rank Tensor Propagation
- Planning under Uncertainty with Factored Actions
- Embedding Knowledge into Learning
Andrew Wrigley, Wee Sun Lee, Nan Ye. Tensor Belief Propagation. International Conference on Machine Learning (ICML) 2017.
Zhan Wei Lim, David Hsu, and Wee Sun Lee. Shortest Path under Uncertainty: Exploration versus Exploitation. Uncertainty in AI (UAI) 2017.
Nguyen Viet Cuong, Wee Sun Lee, Nan Ye. Near-optimal Adaptive Pool-based Active Learning with General Loss. Uncertainty in AI (UAI) 2014.
Haoyu Bai, David Hsu, Wee Sun Lee. Integrated Perception and Planning in Continuous Space: A POMDP Approach. International Journal of Robotics Research, 2014.
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu. Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation. Journal of Machine Learning Research, 2014
Adhiraj Somani, Nan Ye, David Hsu, and Wee Sun Lee. DESPOT: Online POMDP Planning with Regularization. Neural Information Processing Systems (NIPS) 2013.
Wee Sun Lee and Bing Liu. Learning from Positive and Unlabeled Examples Using Weighted Logistic Regression. International Conference on Machine Learning (ICML) 2003
Robert Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee. Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. The Annals of Statistics, 1998
Awards & Honours
- RoboCup Best Paper Award, International Conference on Intelligent Robots and Systems (IROS) 2015
- First Place, Humanitarian Robotics and Automation Technology Challenge (HRATC) 2015
- First place, POMDP track, ICAPS International Probabilistic Planning Competition (IPPC) in 2011 and again in 2014
- Best student paper award, Uncertainty in AI (UAI) 2014 (as faculty co-author)
- First place for English lexical sample task and second place in the English coarse-grained all word task, word sense disambiguation evaluation in Semeval-1 2007
- J.G. Crawford Prize, Australian National University, 1996
- CS5339: Theory and Algorithms for Machine Learning
- CS4246: AI Planning and Decision Making
- CS5446: AI Planning and Decision Making