一、个人简介

2015年本科毕业于哈尔滨工程大学自动化系；2018年硕士毕业于中国科学技术大学自动化系；2022年博士毕业于香港中文大学系统工程与工程管理系，指导老师为苏文藻教授；2022年至2025年，在KTH-瑞典皇家理工学院电气工程与计算机科学系担任博士后，指导老师为Mikael Johansson教授。主要研究方向为最优化算法与理论及其在分布式优化、联邦学习、安全鲁棒计算与隐私保护等领域的应用。

二、学术/研究成果综述

研究成果发表于 IEEE Trans on Signal Processing、IEEE Trans on Automatic Control、Automatica、IEEE Trans on SignalInformation Processing over Networks 等国际权威期刊，以及NeurIPS、ICLR、AISTATS等机器学习领域的重要学术会议。其第一作者论文荣获国际信号处理领域旗舰会议 IEEE International Conference on Acoustics, Speech,Signal Processing （ICASSP 2024）唯一最佳论文奖。

三、论文/著作

[1] Jiaojiao Zhang, Jiang Hu, Mikael Johansson. Non-convex Composite Federated Learning With Heterogeneous Data, Automatica, 2025.

[2] Jiaojiao Zhang, Linglingzhi Zhu, Dominik Fay, Mikael Johansson. Locally Differentially Private Online Federated Learning With Correlated Noise, IEEE Transactions on Signal Processing, 2025.

[3] Yue Huang, Jiaojiao Zhang, Qing Ling. Memory-Efficient Correlated Noise for Locally Differentially Private Momentum in Distributed Learning. IEEE International Workshop on Machine Learning for Signal Processing, IEEE MLSP 2025.

[4] Yue Huang, Jiaojiao Zhang, Qing Ling. Differential Privacy in Distributed Learning: Beyond Uniformly Bounded Stochastic Gradients. Artificial IntelligenceStatistics, AISTATS 2025.

[5] Zesen Wang, Jiaojiao Zhang, Xuyang Wu, Mikael Johansson. From Promise to Practice: Realizing High-performance Decentralized Training. The Thirteenth International Conference on Learning Representations, ICLR 2025.

[6] Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So, Mikael Johansson. Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. The 38th Annual Conference on Neural Information Processing Systems, NeurIPS 2024.

[7] Jiang Hu, Jiaojiao Zhang,Kangkang Deng. Achieving Local Consensus Over Compact Submanifolds. IEEE Transactions on Automatic Control, 2025.

[8] Erik Berglund, Jiaojiao Zhang*, Mikael Johansson. Soft Quasi-Newton: Guaranteed Positive Definiteness by Relaxing the Secant Constraint, Optimization MethodsSoftware, 2025.

[9] Jiaojiao Zhang, Xuechao He, Yue Huang, Qing Ling. Byzantine-RobustCommunication-Efficient Personalized Federated Learning, IEEE Transactions on Signal Processing, 2024.

[10] Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So, Qing Ling. Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning. IEEE Transactions on Signal Processing, 2023.

[11] Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So, Qing Ling. A Penalty Alternating Direction Method of Multipliers for Convex Composite Optimization over Decentralized Networks. IEEE Transactions on Signal Processing, 2021.

[12] Jiaojiao Zhang, Qing Ling, Anthony Man-Cho So. A Newton Tracking Algorithm with Exact Linear Convergence for Decentralized Consensus Optimization. IEEE Transactions on SignalInformation Processing over Networks, 2021.

[13] Jiaojiao Zhang, Shuang Cong, Qing Ling, Kezhi LiHerschel Rabitz. Quantum State Filter with DisturbanceNoise. IEEE Transactions on Automatic Control, 2019.

[14] Jiaojiao Zhang, Shuang Cong, Qing Ling, Kezhi Li. An EfficientFast Quantum State Estimator with Sparse Disturbance. IEEE Transactions on Cybernetics, 2018.

四、联系方式

jiaoz@gbu.edu.cn

https://jiaojiaozhang-jjz.github.io//

五、其他信息

2018年获香港博士研究生奖学金计划（HKPFS），2022年获瑞典数字未来博士后奖学金计划（Digital Futures）, IEEE International Conference on Acoustics, Speech,Signal Processing （ICASSP 2024）最佳论文奖。