I am an assistant professor in the School of Data Science (SDS) at The Chinese University of Hong Kong, Shenzhen. I received a B.S. Degree in Engineering Mechanics from Peking University, in 2012, and a Ph.D. Degree in Systems Engineering from the University of Virginia, in 2016. From 2016 to 2019, I was a postdoc at the University of Florida, Arizona State University and Boston University, respectively.

My research interests mainly lie in distributed optimization, learning and control within networked multi-agent systems. In particular, I have been focusing on studying distributed stochastic gradient methods and optimization over general, directed network topologies. I have also been working on algorithms involving communication compression techniques recently.

Distributed optimization over a directed graph: each node represents an agent with a local cost function. The global objective is to minimize the average of all local cost functions through local computation and local communication with neighbors.

I am always looking for self-motivated students with solid mathematical background and research interests in networks, optimization, machine learning, distributed algorithms, etc.

Recent News



S. Pu, A. Olshevsky and I.C. Paschalidis, A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent, submitted.

Journal Publications

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

S. Pu and A. Nedich. Distributed Stochastic Gradient Tracking Methods. Mathematical Programming, 2020.

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.

S. Pu, W. Shi*, J. Xu and A. Nedich. Push-Pull Gradient Methods for Distributed Optimization in Networks. IEEE Transactions on Automatic Control, 2020.

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.

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

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

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

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

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

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]

(*co-first author)