濮睿智，必威betway西汉姆助理教授。2021年9月至2025年9月就读于加拿大西安大略大学，获得计算机科学博士学位。于腾讯AI Lab（2024/2025），华为诺亚方舟实验室（2022/2025）等科研机构参与多次科研实习。

Dr Ruizhi Pu is an Assistant Professor at the School of Statistics and Data Science, Southeast University. He studied at Western University in Canada from September 2021 to September 2025 and obtained a Doctoral Degree in Computer Science. He participated in multiple research internships at research institutions such as Tencent AI Lab (2024/2025) and Huawei Noah's Ark Lab (2022/2025).

主要研究领域包括鲁棒性机器学习（标签噪声，数据不均衡），联邦学习，图神经网络学习。

Natural Sciences and Engineering Research Council of Canada，Federal Project，Aresearch on modern machine learning technology and theory2021-2025，参与

Huawei Montreal，横向项目: Continual Learning on Intelligent WiFi signal processing. 2021-2025, 参与

[1]Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure. Ruiyi Fang,…, Ruizhi Pu (通讯作者), et al. AAAI-2026, Oral.

[2]Leveraging group classification with descending soft labeling for deep imbalanced regression. Ruizhi Pu, et al. AAAI-2025, Oral.

[3]On the benefits of attribute-driven graph domain adaptation. Ruiyi Fang, .., Ruizhi Pu (通讯作者), et al. ICLR-2025.

[4]Towards more general loss and setting in unsupervised domain adaptation. CJ Shui*, Ruizhi Pu*(共一)，et al. TKDE.

[5]FedELR: When federated learning meets learning with noisy labels. Ruizhi Pu, et al. Neural Networks.

[6]FedFMD: Fairness-Driven Adaptive Aggregation in Federated Learning via Mahalanobis Distance. XiuTing Wen, …, Ruizhi Pu (通讯作者), et al. CIKM 2025.

[7]Unraveling the mysteries of label noise in source-free domain adaptation: Theory and practice. Gezheng Xu,…,Ruizhi Pu, et al. TPAMI.

[8]When source-free domain adaptation meets learning with noisy labels. Yi Li,…, Ruizhi Pu, et al. ICLR-2023 Spotlight.