Biography

I am an assistant professor in the Institute for Data and Decision Analytics (iDDA) 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 focused on designing novel distributed optimization algorithms for problems with uncertainties (stochastic optimization) and general network topologies (directed graphs).


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

Academic Publications

Preprints


S. Pu, A Robust Gradient Tracking Method for Distributed Optimization over Directed Networks, submitted.


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


Journal Papers


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


S. PuA. Olshevsky and I.C. PaschalidisAsymptotic Network Independence In Distributed Stochastic Optimization for Machine Learning, IEEE Signal Processing Magazine, 37(3):114-122.


S. PuW. 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 NetworksIEEE 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 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)