Fangju Wang

My research areas include machine learning, intelligent systems, digital image processing, and algorithm efficiency. Currently I am applying the technique of partially observable Markov decision process (POMDP), along with reinforcement learning, to building intelligent tutoring systems (ITSs).

My work is aimed at developing new techniques/algorithms for handling uncertainties, improving performance, and reducing computing costs.

• artificial intelligence
• machine learning
• reinforcement learning
• partially observable Markov decision process (POMDP)
• intelligent tutoring system
• computing efficiency
• automatic speech recognition

• Fangju Wang, (2019). "A Space-Efficient Technique of Policy Trees for an Intelligent Tutoring System on POMDP". Springer Book of Communications in Computer and Information Science 1022, 251-267.
• Fangju Wang. (2019). "Efficient Computing of the Bellman Equation in a POMDP-based Intelligent Tutoring System". Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019) (1), 15-23.
• Fangju Wang. (2018). "Reinforcement learning in a POMDP-based intelligent tutoring system for optimizing teaching strategies". International Journal of Information and Education Technology, 8(8): 553-558.
• Fangju Wang. (2016). "Minimizing Computing Costs of Policy Trees in a POMDP-Based Intelligent Tutoring System". Springer Book of Communications in Computer and Information Science 739, 159-178.
• William B. Gardner, Gary Grewal, Deborah Stacey, David A. Calvert, Stefan C. Kremer, Fangju Wang. (2015). "A new Canadian interdisciplinary Ph.D. in Computational sciences". Elsevier Journal of Computational Science. 9(2): 82-87.
• Fangju Wang. (2015). "Handling Exponential State Space in a POMDP-Based Intelligent Tutoring System". Proceedings of 6th International Conference on E-Service and Knowledge Management (IIAI ESKM 2015), Okayama, Japan, 2015-07-12, 67-72.
• Fangju Wang and Kyle Swegles. (2013). "Modelling User Behavior Online for Disambiguating User Input in a Spoken Dialogue System". Elsevier Speech Communication. 55(1): 84-98.