姓 名：周帆
职 称：副教授
研究方向：深度学习，强化学习，图网络，因果推断
教授课程：人工智能导论，机器学习
E - mail：zhoufan@mail.shufe.edu.cn
序号 | 项目名称 | 项目编号 | 项目来源 | 起止时间 |
1 | 交通时空网络的统计建模与供需匹配策略设计 | 12001356 | 国家自然科学基金青年项目 | 2021.01-2023.12 |
2 | 基于多目标强化学习的出租车派单策略设计 | 20YF1412300 | 上海市青年科技英才扬帆计划 | 2020.07-2023.06 |
3 | 基于空间排列不变性的动态时空决策模型 | 21CGA44 | 上海市晨光计划 | 2022.02-2024.02 |
4 | 强化学习中的不确定性推理 | 2022RC0AB06 | 之江实验室开放课题 | 2022.02-2024.01 |
深度学习，强化学习，图网络，因果推断
2015-2019 北卡罗来纳大学教堂山分校 生物统计系 博士
2021-至今 韦德官方网站 韦德官方网站 副教授
2019-2021 韦德官方网站 韦德官方网站 助理教授
（*：通讯作者）
1. Kuang, Q., Zhu, Z., Zhang, L., Zhou, F.* (2023). Variance Control for Distributional Reinforcement Learning. The 40th International Conference on Machine Learning. (ICML 2023)
2. Sui, Y., Huang, Y., Zhu, HT., Zhou, F.* (2023). Adversarial Learning of Distributional Reinforcement Learning. The 40th International Conference on Machine Learning. (ICML 2023)
3. Bai, C., Xiao, T., Zhu, Z., Wang, L., Zhou, F., Garg, A., He, B., Liu, P. and Wang, Z. (2022). Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems.
4. Yu, S., …, Zhou, F.* (2022). MDP2 Forest: A Constrained Continuous Multi-dimensional Policy Optimization Approach for Short-video Recommendation. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)
5. Zhou, F., Luo, S., Qie, X., Ye, J., Zhu, HT. (2021). 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.
6. Zhou, F., et.al. (2021). Multi-Objective Distributional Reinforcement Learning for Large-Scale Order Dispatching. The 21st IEEE International Conference on Data Mining (ICDM 2021)
7. Zhou, F., Zhu, Z., Kuang, Q., & Zhang, L. (2021). Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning. The 30th International Joint Conference on Artificial Intelligence (IJCAI 2021)
8. Zhou, F.,Wang, J., Feng, X. (2020). Non-Crossing Quantile Regression for Distributional Reinforcement Learning. The 34th Conference on Neural Information Processing Systems(NeurIPS 2020).
9. Zhou, F., Zhou, H., Li, T., Zhu, HT. (2020). Analysis of Secondary Phenotypes in Multi-Group Association Studies. Biometrics, 76(2), 606-618.
10. Zhou, F., Li, T., Zhou, H., Ye, J., Zhu, HT. (2019). Graph-Based Semi-Supervised Learning with Nonignorable Nonresponses. The 33rd Conference on Neural Information Processing Systems(NeurIPS 2019).
11. Zhao, B., ..., Zhu, HT. including Zhou, F. (2019). GWAS of 19,629 individuals identifies novel genetic variants for regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nature Genetics, 51, 1637-1644. (IF = 27.603)
12. Zhu, W., Yuan, Y., Zhang, J., Zhou, F., Knickmeyer, R, Zhu, HT. (2017). Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study. NeuroImage, 146, 983-1002.
13. Wang, C. W., Lee, Y. C., Calista, E., Zhou, F., et.al. (2017). A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays. Bioinformatics, 34(10), 1767-1773.
2021 2nd place of the Alzheimer Disease classification challenge, PRCV 2021
2020 Barry H. Margolin Award, Department of Biostatistics, UNC-Chapel Hill
2019 New Researcher Award, International Chinese Statistical Association (ICSA)
2019 Travel Award for junior faculties, NeurIPS 2019
2017 The 1st place of the Grand Challenge, ISBI 2017
