Overview
Background
Nan Ye's research interest spans machine learning, statistics and optimization. He has published papers on topics including sequential decision making under uncertainty, weakly supervised learning, probabilistic graphical models, statistical learning theory, in venues such as NeurIPS, ICML, ICLR, UAI, JAIR, JMLR. He received an IJCAI-JAIR Best Paper Prize in 2022, and a UAI Best Student Paper Award in 2014.
He is a Lecturer in Statistics and Data Science in the School of Mathematics and Physics in University of Queensland. He previously held postdoc positions at QUT and UC Berkeley from 2015 to 2018, and at NUS from 2013 to 2014. He obtained his PhD in Computer Science from NUS, and completed double first-class honors in Computer Science and Applied Mathematics, also from NUS.
Please visit his personal webpage for more information: https://yenan.github.io/.
Availability
- Dr Nan Ye is:
- Available for supervision
Research interests
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machine learning
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sequential decision making
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numerical optimization
Works
Search Professor Nan Ye’s works on UQ eSpace
2008
Conference Publication
On preprocessing and antisymmetry in de novo peptide sequencing: Improving efficiency and accuracy
Ning, Kang, Ye, Nan and Leong, Hon Wai (2008). On preprocessing and antisymmetry in de novo peptide sequencing: Improving efficiency and accuracy. Computational Systems Bioinformatics 2007, San Diego, CA United States, 13-17 August 2007. London, United Kingdom: World Scientific Publishing. doi: 10.1142/S0219720008003503
2008
Journal Article
Prescribed learning of indexed families
Jain, Sanjay, Stephan, Frank and Nan, Ye (2008). Prescribed learning of indexed families. Fundamenta Informaticae, 83 (1-2), 159-175.
2007
Conference Publication
Prescribed learning of R.E. classes
Jain, Sanjay, Stephan, Frank and Ye, Nan (2007). Prescribed learning of R.E. classes. 18th International Conference on Algorithmic Learning Theory, Sendai Japan, 1-4 October 2007. Berlin, Germany: Springer. doi: 10.1007/978-3-540-75225-7_9
Supervision
Availability
- Dr Nan Ye is:
- Available for supervision
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Supervision history
Current supervision
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Doctor Philosophy
Data-driven framework for Sequential Decision Making in Operations Research
Principal Advisor
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Doctor Philosophy
Reinforcement Learning for Partially Observable Environments
Principal Advisor
Other advisors: Professor Dirk Kroese
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Doctor Philosophy
Machine Learning for Quantitative Fisheries Stock Assessments
Principal Advisor
Other advisors: Emeritus Professor Jerzy Filar
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Doctor Philosophy
Data-driven framework for Sequential Decision Making in Operations Research
Principal Advisor
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Doctor Philosophy
Reinforcement Learning for Large and Complex Partially Observable Markov Decision Processes
Principal Advisor
Other advisors: Professor Dirk Kroese
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Doctor Philosophy
Offline Reinforcement Learning Theory and Algorithms
Principal Advisor
Other advisors: Professor Fred Roosta
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Master Philosophy
Improved Exploration Methods for Deep Reinforcement Learning
Principal Advisor
Other advisors: Professor Dirk Kroese
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Doctor Philosophy
Breast cancer metastasis prediction via machine learning and spatial cellular pathology
Associate Advisor
Other advisors: Associate Professor Peter Simpson, Dr Quan Nguyen
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Doctor Philosophy
High-stakes Decision Making with Weakly Supervised Data
Associate Advisor
Other advisors: Dr Miao Xu
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Doctor Philosophy
High-stakes Decision Making with Weakly Supervised Data
Associate Advisor
Other advisors: Dr Miao Xu
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Doctor Philosophy
AI/ML Framework for Mixed-integer Nonlinear Optimisation
Associate Advisor
Other advisors: Professor Fred Roosta
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Doctor Philosophy
AI/ML Framework for Mixed-integer Nonlinear Optimisation
Associate Advisor
Other advisors: Professor Fred Roosta
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Doctor Philosophy
Development of novel deep learning methods for medical imaging
Associate Advisor
Other advisors: Professor Feng Liu, Dr Hongfu Sun
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Doctor Philosophy
High-stakes Decision Making with Weakly Supervised Data
Associate Advisor
Other advisors: Dr Miao Xu
Completed supervision
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2022
Doctor Philosophy
Modelling and explaining behaviour with Inverse Reinforcement Learning: Maximum Entropy and Multiple Intent methods
Principal Advisor
Media
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