博士（弗吉尼亚大学）

学士（北京大学）

濮实博士现任香港中文大学（深圳）数据科学学院助理教授。在此之前, 曾任佛罗里达大学、亚利桑那州立大学和波士顿大学博士后研究员。2012年取得北京大学工学学士学位, 2016年取得弗吉尼亚大学系统工程博士学位。主要研究方向为多智能体网络中的分布式优化和机器学习算法。2017年获弗吉尼亚大学Louis T. Rader杰出毕业生荣誉称号，2019年受到IEEE控制与决策会议最佳论文奖委员会主席的邀请参评该奖项。以第一作者在Mathematical Programming、IEEE Transactions on Automatic Control、SIAM Journal on Control and Optimization、Operations Research等运筹优化和控制领域的顶级期刊发表多篇论文，其中一篇代表作入选ESI高被引论文。近期的研究成果受邀以综述形式发表于IEEE旗舰期刊Proceedings of The IEEE。研究工作受到国家自然科学基金委员会、深圳市大数据研究院、深圳市机器人与人工智能研究院等机构的资助。

Preprints

1. Z. Song*, L. Shi, S. Pu and M. Yan, Optimal Gradient Tracking for Decentralized Optimization, submitted.

2. Z. Song*, L. Shi, S. Pu and M. Yan, Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs, preprint.

3. Z. Song*, L. Shi, S. Pu and M. Yan, Compressed Gradient Tracking for Decentralized Optimization over General Directed Networks, submitted.

4. Y. Liao*, Z. Li*, K. Huang* and S. Pu, Compressed Gradient Tracking Methods for Decentralized Optimization with Linear Convergence, submitted.

5. K. Huang* and S. Pu, Improving the Transient Times for Distributed Stochastic Gradient Methods, submitted.

Journal Papers

1. S. Pu, A. Olshevsky and I.C. Paschalidis, A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent, IEEE Transactions on Automatic Control, accepted.

2. S. Pu and A. Nedich. Distributed Stochastic Gradient Tracking Methods. Mathematical Programming, 187(1):409-457, 2021.

3. S. Pu, W. Shi†, J. Xu and A. Nedich. Push-Pull Gradient Methods for Distributed Optimization in Networks. IEEE Transactions on Automatic Control, 66(1):1-16, 2021.

4. R. Xin, S. Pu, A. Nedić and U. Khan. A General Framework for Decentralized Optimization with First-order Methods. Proceedings of the IEEE, 108(11):1869-1889, 2020.

5. S. Pu, A. Olshevsky and I.C. Paschalidis, Asymptotic Network Independence In Distributed Stochastic Optimization for Machine Learning, IEEE Signal Processing Magazine, 37(3):114-122, 2020.

6. S. Pu, J.J. Escudero-Garzas, A. Garcia and S. Shahrampour. An Online Mechanism for Resource Allocation in Networks. IEEE Transactions on Control of Network Systems, 7(3):1140-1150, 2020.

7. S. Pu and A. Garcia. Swarming for Faster Convergence in Stochastic Optimization. SIAM Journal on Control and Optimization, 56(4):2997-3020, 2018.

8. S. Pu and A. Garcia. A Flocking-based Approach for Distributed Stochastic Optimization. Operations Research, 66(1):267-281, 2018.

9. S. Pu, A. Garcia and Z. Lin. Noise Reduction by Swarming in Social Foraging. IEEE Transactions on Automatic Control, 61(12):4007-4013, 2016.

Conference Proceedings

1. S. Pu, A Robust Gradient Tracking Method for Distributed Optimization over Directed Networks, 2020 IEEE 59th Conference on Decision and Control (CDC).

2. S. Pu and A. Nedich. A Distributed Stochastic Gradient Tracking Method. 2018 IEEE 57th Conference on Decision and Control (CDC).

3. S. Pu, W. Shi, J. Xu and A. Nedich. A Push-Pull Gradient Method for Distributed Optimization in Networks. 2018 IEEE 57th Conference on Decision and Control (CDC).

(*(co-)supervised student/postdoc †co-first author