周帆

姓 名：周帆

职　　称：副教授

研究方向：深度学习，强化学习，因果推断，大模型

E-mail： zhoufan@mail.shufe.edu.cn

个人简介：

周帆，上海财经大学统计与数据科学学院副教授，教育部青年长江学者，博士毕业于美国北卡罗来纳大学教堂山分校，现担任统计学顶刊JASA的副主编。研究兴趣包括深度学习，强化学习的算法与理论，时空网络，因果推断，在包括Journal of the American Statistical Association，Journal of Machine Learning Research, NeurIPS, ICML, ICLR等统计学，机器学习顶刊和顶会上发表了数十篇文章，带领团队建立了国际上首个大模型统计推理数据集与评价体系。曾获泛华统计协会国际会议新研究者奖,北卡教堂山分校James E. Grizzle Distinguished Alumnus Award和Barry H. Margolin Award。

招生对象：

博士生招生要求：将来有志于学界的工作；数理基础扎实，自驱力强，热爱科研；熟悉Python以及GPU计算，编程能力强者优先考虑。

硕士生招生要求：将来期望能继续从事博士的学习和研究，或者去业界从事算法与科研相关的岗位。组里硕士同学会参与到由博士生领导的团队进行系统性科研训练，并推荐去到国内国外顶尖高校继续读博深造，或是前往AI相关方向的研究部门实习。

研究项目

研究成果

* 通讯作者; † 指导的学生

部分期刊文章：

[1] Bang Liu, Run Yang†, and Fan Zhou∗. Discussion of “LAMBDA: Large model based data agent”. Journal of the American Statistical Association, 2025.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] Fan Zhou, Haibo Zhou, Tengfei Li, and Hongtu Zhu. Analysis of secondary phenotypes in multigroup association studies. Biometrics, 76(2):606–618, 2020.

[7] 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.

会议文章：

[1] 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).

[2] 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).

[3] Shuguang Yu†, Shuxing Fang†, Ruixin Peng†, Zhenglin 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).

[4] 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).

[5] 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).

[6] Qi Kuang†, Zhoufan Zhu†, Liwen Zhang, and Fan Zhou∗. Variance control for distributional reinforcement learning. In 40th International Conference on Machine Learning. (ICML 2023).

[7] Yang Sui†, Yukun Huang†, Hongtu Zhu, and Fan Zhou∗. Adversarial learning of distributional reinforcement learning. In 40th International Conference on Machine Learning. (ICML 2023).

[8] Sizhe Yu†, Ziyi Liu, Shixiang Wan, Jia Zheng, Zang Li, and Fan Zhou∗. Mdp2 forest: 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.

[9] 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).

[10] 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. IEEE, 2021.

[11] 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.

[12] Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, and Ye Jieping. Graph-based semi-supervised learning with non-ignorable non-response. In 33th Conference on Neural Information Processing Systems (NeurIPS 2019), 32, 2019.

奖励、荣誉

2024 教育部青年长江学者

2023 上海市东方英才青年项目

2022 上海市晨光学者

2021 Alzheimer Disease classification challenge全球亚军, PRCV 2021

2020 Barry H. Margolin Award, Department of Biostatistics, UNC-Chapel Hill

2019 新研究者奖, 泛华统计协会 (ICSA) 国际会议

2019 Travel Award for junior faculties, NeurIPS 2019

2017 The 1st place of the Grand Challenge, ISBI 2017