I am currently an Assistant Professor at School of Statistics and Data Science (SSDS), Southeast University. I received my Ph.D. degree from the University of Technology Sydney (UTS). My recent research interests include Fairness and Bayesian Statistics. I am also interested in topics about Bias mitigation in LLM and AI4Science. Feel free to reach out to discuss potential collaborations in any related field or area of mutual interest.

News

• Our paper got accepted to TPAMI
• Honored to be selected as one of the recipients of the Best Reviewer award at AISTATS 2025
• One paper accepted at AAAI 2025
• One paper accepted at KDD 2025
• Recognized as one of the Top Reviewers for NeurIPS 2024

Preprint

• Q. Kong, Y. Zhang, Y. Liu, P. Tong, E. Liu, F. Zhou, “Language-TPP: Integrating Temporal Point Processes with Language Models for Event Analysis.” [paper]
• F. Zhou, Q. Kong, Y. Zhang, “Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches.” [paper]
• Y. Zhang and F. Zhou, “Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency.” [paper]
• Z. Zhao, L. Cao and Y. Zhang, “Out-of-Distribution Knowledge Distillation via Confidence Amendment.” [paper]

Selected Publications

• Z. Zhao, L. Cao, Y. Zhang, K. Lin, W. Zheng, “Distilling the Unknown to Unveil Certainty,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025. 
• Y. Zhang, Z. Li, Y. Wang, F. Chen, X. Fan and F. Zhou, “Navigating Towards Fairness with Data Selection,” accepted at AAAI 2025. [paper][code]
• J. Lyu, Y. Zhang, X. LU and F. Zhou, “Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression,” accepted at KDD 2025. [paper][code]
• Z. Sun*, Y. Zhang*, Z. Ling, X. Fan and F. Zhou, “Nonstationary Sparse Spectral Permanental Process,” NeurIPS 2024. [paper][code]
• Z. Ling, L. Li, Z. Feng, Y. Zhang, F. Zhou, R. Qiu and Z. Liao, “Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures,” ICML 2024. [paper][code]
• Y. Zhang, B. Li, Z. Ling and F. Zhou, “Mitigating Label Bias in Machine Learning: Fairness through Confident Learning,” AAAI 2024. [paper][code]
• Y. Zhang, Q. Kong and F. Zhou, “Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes,” NeurIPS 2023. [paper][code]
• Y. Zhang, F. Zhou, Z. Li, Y. Wang and F. Chen, “Fair Representation Learning with Unreliable Labels,” AISTATS 2023. [paper][code]
• Y. Zhang, F. Zhou, Z. Li, Y. Wang and F. Chen, “Bias-Tolerant Fair Classification,” ACML 2021. [paper][code]
• F. Zhou, Q. Kong, Z. Deng, J. Kan, Y. Zhang, C. Feng and J. Zhu, “Efficient Inference for Dynamic Flexible Interactions of Neural Populations,” JMLR 2022. [paper][code]
• F. Zhou, Y. Zhang and J. Zhu, “Efficient Inference of Flexible Interaction in Spiking-neuron Networks,” ICLR 2021. [paper][code]

* indicates equal contribution

Academic Services

ICML, AISTATS, NeurIPS, ICLR, TNNLS, TMLR