Title Distributed Optimization and Cooperative Learning over Networks Biography Zhengtao Ding received B.Eng. degree from Tsinghua University, Beijing, China, and M.Sc. degree in systems and control, and the Ph.D. degree in control systems from the University of Manchester Institute of Science and Technology, Manchester, U.K. After working in Singapore for ten years, he joined the University of Manchester in 2003, where he is currently Professor of Control Systems with the Department of Electrical and Electronic Engineering. He is the author of the book: Nonlinear and Adaptive Control Systems (IET, 2013) and has published over 300 research articles. His research interests include nonlinear and adaptive control theory and their applications, more recently on distributed optimization and distributed machine learning, with applications to power systems and robotics. Prof. Ding has served as the Subject Chef Editor of Nonlinear Control for Frontiers, and Associate Editor for the IEEE Transactions on Automatic Control, IEEE Control Systems Letters, and several other journals. He is a member of IEEE Technical Committee on Nonlinear Systems and Control, IEEE Technical Committee on Intelligent Control, and IFAC Technical Committee on Adaptive and Learning Systems. Abstract There are many challenges and opportunities, such as internet of things, big data, machine learning, smart grid, in the area of network-connected systems and control applications, in particular, in the areas relating to distributed learning, optimization, decision making and control. Recent advances in distributed networks along with the development of complex and large-scale subsystems have significantly incentivized coordination and cooperation over multi-agent systems. Many distributed algorithms have been developed in the areas relating to distributed machine learning, optimization and differential games, which aim at making decisions in local level, and achieving certain global objectives through network communications. Certain control perspectives such as convergence, nonlinearity, adaptation and consensus are clearly essential in the design and analysis of the distributed algorithms. This talk will cover some recent activities in relation to multi-agent applications carried out in the speaker’s group in University of Manchester, including distributed optimization using algorithms based on multi-agents, cooperative and competitive machine learning over networks, applications of distributed optimization and machine learning algorithms to power systems and smart girds such as optimal power dispatch etc. The speaker will review some fundamental concepts of network-connected dynamic systems and basic analytic tools for consensus-based distributed algorithms, and the presentation will focus on distributed algorithms, and motivations to the algorithm design from distributed perspectives.