About Me
I have joined in the NUS Ubicomp Lab on 14 Oct. 2024 as a research fellow working with Prof. Brian Y. Lim. I received my PhD. degree in Computer Science and Technology at University of Science and Technology of China (USTC) in 2024, advised by Prof. Enhong Chen and Prof. Qi Liu. I received my bachelor’s degree in Computer Science and Technology at USTC in 2018.
I have a strong interest in how humans and AI systems can coexist and collaborate in ways that are not only efficient, but also interpretable, reliable, and aligned with human needs. Broadly, my research aims to develop human-compatible AI systems that are technically effective, interpretable, adaptable, and useful in human-AI collaboration. My doctoral work approached this goal from the model side through interpretable and adaptive user modeling. In my current postdoctoral research at NUS, I am expanding my background toward the human side by studying how people understand, use, and collaborate with AI systems. I am currently working on topics related to improving human–LLM collaboration.
I am currently on the job market and actively seeking postdoctoral or related research opportunities. If you think my background could be a good fit for your group or organization, I would be happy to hear from you.
News
- 2026.03: 🎉🎉 Two papers accepted by CHI 2026.
- 2026.01: 🎉🎉 One paper accepted by IUI 2026.
- 2025.05: 🎉🎉 One paper accepted by ACL 2025.
- 2025.01: 🎉🎉 One paper accepted by IEEE Transactions on Knowledge and Data Engineering.
- 2024.10: I join in the NUS Ubicomp Lab.
Selected Publications
A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions
Fei Wang, Weibo Gao, Qi Liu, Jiatong Li, Guanhao Zhao, Zheng Zhang, Zhenya Huang, Mengxiao Zhu, Shijin Wang, Wei Tong, Enhong Chen
Preprint [paper]
Transferable XAI: Relating Understanding Across Domains with Explanation Transfer
Fei Wang, Yifan Zhang, Brian Y Lim
IUI 2026 [paper]
Beyond Scores: Explainable Intelligent Assessment Strengthens Pre-service Teachers' Assessment Literacy
Yuang Wei, Fei Wang, Yifan Zhang, Brian Y. Lim, Bo Jiang
CHI 2026 [paper]
Comparables XAI: Faithful Example-based AI Explanations with Counterfactual Trace Adjustments
Yifan Zhang, Tianle Ren, Fei Wang, Brian Y. Lim
CHI 2026 [paper]
Unified Uncertainty Estimation for Cognitive Diagnosis Models
Fei Wang, Qi Liu, Enhong Chen, Chuanren Liu, Zhenya Huang, Jinze Wu, Shijin Wang
WWW 2024 [paper] [code]
Dynamic Cognitive Diagnosis: An Educational Priors-Enhanced Deep Knowledge Tracing Perspective
Fei Wang, Zhenya Huang, Qi Liu, Enhong Chen, Yu Yin, Jianhui Ma, Shijin Wang
IEEE Transactions on Learning Technologies, 2023, 16(3): 306-323 [paper] [code]
NeuralCD: a general framework for cognitive diagnosis
Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yu Yin, Shijin Wang, Yu Su
Transactions on Knowledge and Data Engineering, 2023. 35(8):8312-8327 [paper] [code]
FairLISA: Fair User Modeling with Limited Sensitive Attributes Information
Zheng Zhang, Qi Liu, Hao Jiang, Fei Wang, Yan Zhuang, Le Wu, Weibo Gao, Enhong Chen
NeurIPS 2023 [paper]
HierCDF: A Bayesian Network-based Hierarchical Cognitive Diagnosis Framework
Jiatong Li, Fei Wang, Qi Liu, Mengxiao Zhu, Wei Huang, Zhenya Huang, Enhong Chen, Yu Su, Shijin Wang
SIGKDD 2022 [paper]
Neural cognitive diagnosis for intelligent education systems
Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, Shijin Wang
AAAI 2020 [paper] [code] [slide]
Modeling context-aware features for cognitive diagnosis in student learning
Yuqiang Zhou, Qi Liu, Jinze Wu, Fei Wang, Zhenya Huang, Wei Tong, Hui Xiong, Enhong Chen, Jianhui Ma
SIGKDD 2021 [paper]