教育背景

2016.09-2020.03，澳大利亚新南威尔士大学计算机科学专业，工学博士学位

2011.09-2014.07，中国科公司大学电气工程及其自动化专业，工学硕士学位

2007.09-2011.07，北京林业大学电气工程及其自动化专业，工学学士学位

 

工作经历

2022.09至今，太阳成集团tyc122cc，讲师

2020.08-2022.08，清华大学计算机系，博士后

 

研究方向

统计机器学习、贝叶斯方法、大语言模型、AI4Science

 

荣誉奖励

1. 太阳成集团tyc122cc优选论文，第一获奖人（通讯作者），太阳成集团tyc122cc，2025

2. 北京市高校优质本科课件，第二获奖人，北京市教育委员会，2024

 

科研项目

1. 基于标准化流的灵活高效Hawkes过程，国家自然科学基金，青年基金，主持，2022-2024

2. 时变系统中的霍克斯过程，中国博士后科学基金，面上资助，主持，2021-2022

3. 非参数点过程及其应用，中国博士后科学基金，特别资助，主持，2020-2022

4. 贝叶斯非参数点过程，中国博士后科学基金，国际交流计划引进项目，主持，2020-2022

5. 北京市未来区块链与隐私计算高精尖创新项目，未来区块链与隐私计算高精尖中心，参与，2024至今

 

教改项目

1. 《最优化方法》在线课程建设项目，校级，项目参与人，2024

2. 《最优化方法》123金课项目，校级，项目参与人，2023

3. 《非参数统计》“十四五”规划教材项目，校级，项目参与人，2023

4. 《非参数统计》123金课项目，校级，项目参与人，2022

 

科研发表

1. Junliang Lyu, Yixuan Zhang, Xiaoling Lu, Feng Zhou*(2025), “Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression”. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Toronto Canada.

2. Yixuan Zhang, Zenan Ling, Yang Wang, Fang Chen, Xuhui Fan, Feng Zhou*(2025), “Navigating Towards Fairness with Data Selection”, In AAAI Conference on Artificial Intelligence, Philadelphia USA.

3. Haoqun Cao, Zizhuo Meng, Tianjun Ke, Feng Zhou*(2024), “Is Score Matching Suitable for Estimating Point Processes?”, In Annual Conference on Neural Information Processing Systems, Vancouver Canada.

4. Zicheng Sun, Yixuan Zhang, Zenan Ling, Xuhui Fan, Feng Zhou*(2024), “Nonstationary Sparse Spectral Permanental Process”, In Annual Conference on Neural Information Processing Systems, Vancouver Canada.

5. Zeyue Zhang, Xiaoling Lu, Feng Zhou*(2024), “Conjugate Bayesian Two-step Change Point Detection for Hawkes Process”, In Annual Conference on Neural Information Processing Systems, Vancouver Canada.

6. Zizhuo Meng, Ke Wan, Yadong Huang, Zhidong Li, Yang Wang, Feng Zhou*(2024), “Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks”. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona Spain.

7. Tianjun Ke, Haoqun Cao, Feng Zhou*(2024), “Accelerating Convergence in Bayesian Few-Shot Classification”, In International Conference on Machine Learning, Vienna Austria.

8. Yixuan Zhang, Boyu Li, Zenan Ling, Feng Zhou*(2024), “Mitigating Label Bias in Machine Learning: Fairness through Confident Learning”, In AAAI Conference on Artificial Intelligence, Vancouver Canada.

9. Yibo Miao, Yu Lei, Feng Zhou*, Zhijie Deng*(2024), “Bayesian Exploration of Pre-trained Models for Low-shot Image Classification”, In IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle USA.

10. Zizhuo Meng, Boyu Li, Xuhui Fan, Zhidong Li, Yang Wang, Fang Chen, Feng Zhou*(2024), “TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes”, In European Conference on Artificial Intelligence, Santiago de Compostela.

11. Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert Qiu, Zhenyu Liao*(2024), “Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures”, In International Conference on Machine Learning, Vienna Austria.

12. Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu (2024), “Calibrating Deep Ensemble through Functional Variational Inference”, Transactions on Machine Learning Research.

13. Tianjun Ke, Haoqun Cao, Zenan Ling, Feng Zhou*(2023), “Revisiting Logistic-softmax Likelihood in Bayesian Meta-learning for Few-shot Classification”, In Annual Conference on Neural Information Processing Systems, New Orleans USA.

14. Yixuan Zhang, Quyu Kong, Feng Zhou*(2023), “Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes”, In Annual Conference on Neural Information Processing Systems, New Orleans USA.

15. Yixuan Zhang, Feng Zhou*, Zhidong Li, Yang Wang, Fang Chen (2023), “Fair Representation Learning with Unreliable Labels”, In International Conference on Artificial Intelligence and Statistics, Valencia Spain.

16. Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu*(2023), “Heterogeneous Multi-task Gaussian Cox Processes”, Machine Learning, 112(12), 5105-5134.

17. Yongli Mou, Jiahui Geng, Feng Zhou*, Oya Beyan, Chunming Rong, Stefan Decker (2023), “pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints”, In Pacific-Asia Conference on Knowledge Discovery and Data Mining, Osaka Japan.

18. Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu*(2022), “Efficient Inference for Dynamic Flexible Interactions of Neural Populations”, Journal of Machine Learning Research, 23(211), 1-49.

19. Zhijie Deng, Feng Zhou, Jun Zhu*(2022), “Accelerated Linearized Laplace Approximation for Bayesian Deep Learning”, In Annual Conference on Neural Information Processing Systems, New Orleans USA.

20. Feng Zhou, Yixuan Zhang, Jun Zhu*(2021), “Efficient Inference of Flexible Interaction in Spiking-neuron Networks”, In International Conference on Learning Representations, Virtual.

21. Feng Zhou, Simon Luo, Zhidong Li*, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen (2021), “Efficient EM-Variational Inference for Nonparametric Hawkes Process”, Statistics and Computing, 31(4), 46.

22. Xuhui Fan, Bin Li, Feng Zhou, Scott Sisson (2021), “Continuous-Time Edge Modelling Using Non-Parametric Point Processes”, In Annual Conference on Neural Information Processing Systems, Virtual.

23. Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen (2021), “Bias-Tolerant Fair Classification”, In Asian Conference on Machine Learning, Virtual.

24. Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen (2020), “Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables”, Journal of Machine Learning Research, 21(241), 1-31.

 

专利发明

1. 数据处理方法、电子设备及计算机可读储存介质，202110586509.9，国家知识产权局，2021

 

教学课程

1. 本科课程：最优化方法、数据科学专题、新生研讨课

2. 研究生课程：现代优化方法

 

社会兼职

1. 2025.06-2029.07，中国商业统计学会人工智能分会，副秘书长

2. 2025.06-2028.06，Statistics and Computing，副编辑

3. 2025.06-2028.06，Journal of Machine Learning Research，审稿人编辑委员会委员

4. 2025.06-2028.06，全国工业统计学教学研究会青年统计学家协会，理事会理事

 

学术报告

1. Is Score Matching Suitable for Estimating Point Processes?，第七届国际计量经济学与统计学会议，北京，邀请报告

2. Accelerating Convergence in Bayesian Few-shot Classification，第三届全国统计与数据科学联合会议，杭州，邀请报告

3. Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks，第十三届全国概率统计会议，厦门，邀请报告

 

其他

担任NeurIPS、AISTATS、IJCNN领域主席，IJCAI、PAKDD高级程序委员会委员，常年担任ICML、NeurIPS、ICLR、KDD、AAAI、AISTATS等国际会议及JMLR、TNNLS、MLJ、PR、JCGS等期刊审稿专家