[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.

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.