Ph.D. Systems Engineering, University of Virginia, 2016
B.Sc. Engineering Mechanics,Peking University, 2012
Prof. Shi Pu is an Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Prof. Pu earned his Ph.D. in Systems Engineering from the University of Virginia in 2016, and joined us from Boston University where he held a postdoctoral associate position. From 2016 to 2018, he conducted postdoctoral research at the University of Florida and then Arizona State University. His research interests mainly lie in distributed optimization, learning and control within networked multi-agent systems. He has published in top journals such as Operations Research, Mathematical Programming and IEEE Transactions on Automatic Control.
1. S. Pu, A. Olshevsky and I.C. Paschalidis, A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent, SIAM Journal on Optimization, submitted.
1. S. Pu and A. Nedich. Distributed Stochastic Gradient Tracking Methods. Mathematical Programming, accepted.
2. S. Pu, A. Olshevsky and I.C. Paschalidis, Asymptotic Network Independence In Distributed Stochastic Optimization for Machine Learning, IEEE Signal Processing Magazine, accepted.
3. S. Pu, W. Shi*, J. Xu and A. Nedich. Push-Pull Gradient Methods for Distributed Optimization in Networks. IEEE Transactions on Automatic Control, 2020.
4. 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, 2020.
5. S. Pu and A. Garcia. Swarming for Faster Convergence in Stochastic Optimization. SIAM Journal on Control and Optimization, 56(4):2997-3020, 2018.
6. S. Pu and A. Garcia. A Flocking-based Approach for Distributed Stochastic Optimization. Operations Research, 66(1):267-281, 2018.
7. S. Pu, A. Garcia and Z. Lin. Noise Reduction by Swarming in Social Foraging. IEEE Transactions on Automatic Control, 61(12):4007-4013, 2016.
1. S. Pu and A. Nedich. A Distributed Stochastic Gradient Tracking Method. 2018 IEEE 57th Conference on Decision and Control (CDC). [arxiv]
2. 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). [arxiv]