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I am an Associate Professor in the School of Statistics and Data Science at Shanghai University of Finance and Economics (SHUFE). I received my Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill, advised by Prof. Hongtu Zhu and Prof. Haibo Zhou. I currently serve as an Associate Editor of the Journal of the American Statistical Association (JASA). My research focuses on developing statistical and machine learning methods for complex data, with applications in reinforcement learning, spatiotemporal networks, and causal inference.I have published numerous papers in top statistics and machine learning venues including JASA, JMLR, NeurIPS, ICML, and ICLR. I was awarded the New Researcher Award at the International Chinese Statistical Association (ICSA) Conference, the James E. Grizzle Distinguished Alumnus Award, and the Barry H. Margolin Award from UNC-Chapel Hill.
Research Group—StatAI Lab
Selected Publications
* Corresponding author † Students advised by Fan Zhou
Journal Articles
- Qi Kuang†, Chao Wang, Yuling Jiao* and Fan Zhou*. Distributional Off-Policy Evaluation with Deep Quantile Process Regression. Journal of the American Statistical Association, 2026+. [PDF]
- Bang Liu, Run Yang†, and Fan Zhou*. Discussion of "LAMBDA: Large model based data agent". Journal of the American Statistical Association, 2025+. [PDF]
- Chengchun Shi, Zhengling Qi*; Jianing Wang†, and Fan Zhou*.
Value enhancement of reinforcement learning via efficient and robust trust region optimization.
Journal of the American Statistical Association, 119(547):2011-2025, 2024. [PDF]
- Xingdong Feng, Yuling Jiao, Lican Kang, Baqun Zhang, and Fan Zhou*.
Over-parameterized deep nonparametric regression for dependent data with its applications to reinforcement learning.
Journal of Machine Learning Research, 24(383):1-40, 2023. [PDF]
- Fan Zhou, Shikai Luo, Xiaohu Qie, Jieping Ye, and Hongtu Zhu*.
Graph-based equilibrium metrics for dynamic supply-demand systems with applications to ride-sourcing platforms.
Journal of the American Statistical Association, 116(536):1688-1699, 2021. [PDF]
- , Run Yang†, Runpeng Dai†, Siran Gao†, Xiaocheng Tang, Fan Zhou*, and Hongtu Zhu*.
Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing.
Journal of Computational and Graphical Statistics, 2026+. [PDF]
- Chenjia Bai, Ting Xiao, Zhoufan Zhu†, Lingxiao Wang, Fan Zhou, Animesh Garg, Bin He, Peng Liu, and Zhaoran Wang.
Monotonic quantile network for worst-case offline reinforcement learning.
IEEE Transactions on Neural Networks and Learning Systems, 2022. [PDF]
- Fan Zhou, Haibo Zhou, Tengfei Li, and Hongtu Zhu.
Analysis of secondary phenotypes in multigroup association studies.
Biometrics, 76(2):606-618, 2020. [PDF]
- Bingxin Zhao, Tianyou Luo, Tengfei Li, Yun Li, Jingwen Zhang, Yue Shan, Xifeng Wang, Liuqing Yang, Fan Zhou, Ziliang Zhu, et al.
Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits.
Nature Genetics, 51(11):1637-1644, 2019. [PDF]
Conference Papers
- Qi Kuang†, Jiayi Wang, Fan Zhou*, and Zhengling Qi*.
Breaking the order barrier: Off-policy evaluation for confounded POMDPs.
In 39th Conference on Neural Information Processing Systems (NeurIPS 2025). [PDF]
- Shuguang Yu†, Wenqian Xu†, Xinyi Zhou†, Xuechun Wang†, Hongtu Zhu, and Fan Zhou*.
Enhancing prediction performance through influence measure.
In 13th International Conference on Learning Representations (ICLR 2025). [PDF]
- Shuguang Yu†, Shuxing Fang†, Ruixin Peng†, Zhengling Qi, Fan Zhou*, and Chengchun Shi.
Two-way deconfounder for off-policy evaluation under unmeasured confounding.
In 38th Conference on Neural Information Processing Systems (NeurIPS 2024). [PDF]
- Run Yang†, Yuling Yang†, Fan Zhou*, and Qiang Sun*.
Directional diffusion model for graph representation learning.
In 37th Conference on Neural Information Processing Systems (NeurIPS 2023). [PDF]
- Ting Li, Chengchun Shi, Jianing Wang†, Fan Zhou, and Hongtu Zhu*.
Optimal dynamic treatment allocation for efficient policy evaluation.
In 37th Conference on Neural Information Processing Systems (NeurIPS 2023). [PDF]
- Qi Kuang†, Zhoufan Zhu†, Liwen Zhang, and Fan Zhou*.
Variance control for distributional reinforcement learning.
In 40th International Conference on Machine Learning (ICML 2023). [PDF]
- Yang Sui†, Yukun Huang†, Hongtu Zhu, and Fan Zhou*.
Adversarial learning of distributional reinforcement learning.
In 40th International Conference on Machine Learning (ICML 2023). [PDF]
- Sizhe Yu†, Ziyi Liu, Shixiang Wan, Jia Zheng, Zang Li, and Fan Zhou*.
MDP2Forest: A constrained continuous multi-dimensional policy optimization approach for short-video recommendation.
In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), pages 2388-2398, 2022. [PDF]
- Fan Zhou, Zhoufan Zhu†, Qi Kuang†, and Zhang Liwen.
Non-decreasing quantile function network with efficient exploration for distributional reinforcement learning.
In 40th International Joint Conference on Artificial Intelligence (IJCAI 2021). [PDF]
- Fan Zhou, Chenfan Lu†, Xiaocheng Tang, Fan Zhang, Zhiwei Qin, Jieping Ye, and Hongtu Zhu*.
Multi-objective distributional reinforcement learning for large-scale order dispatching.
In 2021 IEEE International Conference on Data Mining (ICDM 2021), pages 1541-1546. [PDF]
- Fan Zhou, Jianing Wang†, and Xingdong Feng.
Non-crossing quantile regression for distributional reinforcement learning.
In 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 33:15909-15919, 2020. [PDF]
- Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, and Ye Jieping.
Graph-based semi-supervised learning with non-ignorable non-response.
In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 32, 2019. [PDF]
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