教育经历2020.09 - 2025.06, 博士, 计算机科学与技术, 南京大学. (LAMDA研究所，导师：张利军教授) 2016.09 - 2020.06, 学士, 计算机科学与技术(实验班), 西安交通大学. 海外交流经历 2023.10 - 2024.05, 联合培养博士研究生, 新加坡国立大学. 2019.01 - 2019.06, 交换生, 美国加州大学伯克利分校. 2018.07 - 2018.08, 交换生, 英国曼彻斯特大学. 工作经历2025.10 至今, 南京理工大学, 计算机科学与工程学院, 教授. 指导学科研究方向：机器学习, 随机优化. 社会、学会及学术兼职会议审稿人: ICML 2025,2024,2023,2022; NeurIPS 2024,2023,2022; ICLR 2025,2024; AAAI 2025; AISTATS 2023. 期刊审稿人: IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Transactions on Information Forensics & Security; IEEE Transactions on Evolutionary Computation; Machine Learning; Applied Numerical Mathematics; Information Sciences; Neurocomputing; TMLR. 出版专著和教材科研创新科研项目复合损失函数的分布式学习. 江苏省研究生科研创新计划 (KYCX24_0231), 2024.05-2025.05. 教学活动发表论文1. Optimizing Unnormalized Statistical Models through Compositional Optimization W. Jiang, J. Qin, L. Wu, C. Chen, T. Yang, and L. Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2025), to appear, 2025. 2. Revisiting Stochastic Multi-Level Compositional Optimization W. Jiang, S. Yang, Y. Wang, T. Yang, and L. Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2025), 47(7): 5613 - 5624, 2025. 3. Normalized Adaptive Variance Reduction Method W. Jiang, S. Yang, Y. Wang, and L. Zhang Journal of Software, 36(11): 4893 - 4905, 2025. 4. Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions W. Jiang, S. Yang, Y. Wang, and L. Zhang In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 22047 - 22080, 2024. 5. Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction W. Jiang, S. Yang, W. Yang, and L. Zhang In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 33891 - 33932, 2024. 6. Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization W. Jiang, S. Yang, W. Yang, Y. Wang, Y. Wan, and L. Zhang In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 21962 - 21987, 2024. 7. Learning Unnormalized Statistical Models via Compositional Optimization W. Jiang, J. Qin, L. Wu, C. Chen, T. Yang, L. Zhang In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), pages 15105 - 15124, 2023. 8. Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization W. Jiang, G. Li, Y. Wang, L. Zhang, and T. Yang In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pages 32499 - 32511, 2022. 9. Optimal Algorithms for Stochastic Multi-Level Compositional Optimization W. Jiang, B. Wang, Y. Wang, L. Zhang, and T. Yang In Proceedings of the 39th International Conference on Machine Learning (ICML 2022), pages 10195 - 10216, 2022. 指导学生情况其他信息获奖、荣誉称号
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