Education

Ph.D. in Computer Science, University of New South Wales, Australia, Sep. 2016 – Mar. 2020

M.Eng. in Electrical Engineering, University of Chinese Academy of Sciences, China, Sep. 2011 – Jul. 2014

B.Eng. in Electrical Engineering, Beijing Forestry University, China, Sep. 2007 – Jul. 2011

 

 

Work Experience

Assistant Professor, School of Statistics, Renmin University of China, Sep. 2022 – Present

Postdoctoral Research Fellow, Department of Computer Science, Tsinghua University, Aug. 2020 – Aug. 2022

 

 

Research Interests

Statistical Machine Learning, Bayesian Methods, Large Language Models, AI for Science

 

 

Honors and Awards

1. Outstanding Paper Award, School of Statistics, Renmin University of China - First Awardee (Corresponding Author), 2025

2. Excellent Undergraduate Courseware Award for Beijing Universities - Second Awardee, Beijing Municipal Education Commission, 2024

 

 

Funding

1. Flexible and Efficient Hawkes Processes Based on Normalizing Flows, National Natural Science Foundation of China (NSFC) - PI, Youth Program, 2022-2024

2. Hawkes Processes in Time-Varying Systems, China Postdoctoral Science Foundation - PI, General Grant, 2021-2022

3. Nonparametric Point Processes and Their Applications, China Postdoctoral Science Foundation - PI, Special Grant, 2020-2022

4. Bayesian Nonparametric Point Processes, China Postdoctoral Science Foundation - PI, International Exchange Program (Incoming Project), 2020-2022

5. Beijing Cutting-Edge Innovation Program on Blockchain and Privacy Computing, Center for Advanced Innovation on Blockchain and Privacy Computing - Participant, 2024-Present

 

 

Educational Reform Project

1. “Optimization Methods” Online Course Development Project, University-Level, Project Contributor, 2024

2. “Optimization Methods” National First-Class Undergraduate Course (123 Gold Course), University-Level, Project Contributor, 2023

3. “Nonparametric Statistics” Textbook Development under the 14th Five-Year Plan, University-Level, Project Contributor, 2023

4. “Nonparametric Statistics” National First-Class Undergraduate Course (123 Gold Course), University-Level, Project Contributor, 2022

 

 

Publications

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.

 

 

Patents

1. Data Processing Method, Electronic Device, and Computer-Readable Storage Medium, Patent No. 202110586509.9, China National Intellectual Property Administration, 2021

 

 

Teaching

1. Undergraduate Courses: Optimization Methods, Topics in Data Science, Freshman Seminar

2. Graduate Course: Modern Optimization Methods

 

 

Professional Service

1. Deputy Secretary-General, AI Subcommittee, China Business Statistics Society, Jun. 2025 - Jul. 2029

2. Associate Editor, Statistics and Computing, Jun. 2025 - Jun. 2028

3. Editorial Board of Reviewers, Journal of Machine Learning Research, Jun. 2025 - Jun. 2028

4. Council Member, Youth Association of the National Teaching and Research Society of Industrial Statistics, Jun. 2025 - Jun. 2028

 

 

Invited Talks

1. Is Score Matching Suitable for Estimating Point Processes?, Invited Talk, 7th International Conference on Econometrics and Statistics (EcoSta 2025), Beijing

2. Accelerating Convergence in Bayesian Few-shot Classification, Invited Talk, 3rd National Conference on Statistics and Data Science, Hangzhou

3. Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks, Invited Talk, 13th National Conference on Probability and Statistics, Xiamen

 

 

Others

Served as Area Chair for NeurIPS, AISTATS, and IJCNN; Senior Program Committee Member for IJCAI and PAKDD.

Regular reviewer for top-tier conferences including ICML, NeurIPS, ICLR, KDD, AAAI, and AISTATS, as well as journals such as Journal of Machine Learning Research (JMLR), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Machine Learning (MLJ), Pattern Recognition (PR), and Journal of Computational and Graphical Statistics (JCGS).