Xuyang Wu received the Bachelor of Science degree in Applied Mathematics from Northwestern Polytechnical University, China, in 2015, and the Ph.D. degree in Communication and Information Systems from the University of Chinese Academy of Sciences, China, in 2020. From 2020 - 2023, He was a postdoctoral researcher at KTH Royal Institute of Technology, Sweden. He is currently an associate professor in the School of  Automation and Intelligent Manufacturing (AiM, Former: School of System Design and Intelligent Manufacturing), Southern University of Science and Technology, Shenzhen, China. His research interests include distributed and large-scale optimization, machine learning, and related areas. He has published 7 first-authored papers on top-tier journals in the control society and AI conferences, including 5 papers on IEEE Transactions on Automatic Control (IEEE TAC) and Automatica, and 2 papers on International Conference on Machine Learning (ICML).

Learn more: http://xuyangwu.github.io

Education Background

◆ Sep. 2015 - Aug. 2020
Ph.D student, Communication and Information Systems, The University of Chinese Academy of Sciences, China.

◆ Sep. 2011 - Jul. 2015

B.S. student, Applied Mathematics, Northwestern Polytechnical University, China.

Working Experience

◆ Jun. 2025 - present.
Associate Professor, School of Automation and Intelligent Manufacturing (AiM), Southern University of Science and Technology, China.

◆ Feb. 2024 - Jun. 2025.

Assistant Professor, School of Automation and Intelligent Manufacturing (AiM), Southern University of Science and Technology, China.
◆ Dec. 2023 - Jan. 2024.

Visiting Scholar, Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong
◆ Dec. 2020 - Nov. 2023
Postdoctoral Researcher, Division of Decision and Control Systems, KTH Royal Institute of Technology, Sweden.

Research Area

Distributed and large-scale optimization, machine learning, and related areas.

Publications

[1] X. Wu, S. Magnusson, and M. Johansson. “Distributed Safe Resource Allocation using Barrier Functions”, Automatica, 2023.

[2] X. Wu, H.R. Feyzmahdavian, S. Magnusson, M. Johansson. “Delay-adaptive Step-sizes for Asynchronous Learning”, Proc. International Conference on Machine Learning (ICML), 2022.

[3] X. Wu, C. Liu, S. Magnusson, M. Johansson. “Delay-agnostic Asynchronous Coordinate Update Algorithm”, Proc. International Conference on Machine Learning (ICML), 2023.

[4] X. Wu, H. Wang, and J. Lu. “Distributed Optimization with Coupling Constraints”, IEEE Transactions on Automatic Control, 2023.

[5] X. Wu and J. Lu. “A Unifying Approximate Method of Multipliers for Distributed Composite Optimization”, IEEE Transactions on Automatic Control, 2023.

[6] X. Wu, Z. Qu, and J. Lu. “A Second-Order Proximal Algorithm for Consensus Optimization”, IEEE Transactions on Automatic Control, 2021.

[7] X. Wu and J. Lu. “Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks”, IEEE Transactions on Automatic Control, 2019.