Rate My Professor Xiaowei Zhao

Professor Xiaowei Zhao is Professor of Control Engineering in the School of Engineering at the University of Warwick. He obtained his PhD in Control Theory from Imperial College London in 2010 and worked as a postdoctoral researcher in the Control Engineering Group at the University of Oxford until 2013. He joined the University of Warwick thereafter and was promoted to Professor of Control Engineering in 2018. Currently, he serves as the director of the EPSRC Supergen Network Plus in Artificial Intelligence for Renewable Energy and co-director of the EPSRC Supergen Offshore Renewable Energy Hub. From 2021 to 2025, he was a member of the Science Expert Group at the UK Government’s Department for Energy Security and Net Zero. In 2024, he was a finalist for the inaugural Manchester Prize, a prestigious government challenge prize recognizing breakthroughs in artificial intelligence for the public good.

Professor Zhao's research specializations encompass control theory and machine learning with applications in offshore renewable energy systems, energy storage, smart grids, and autonomous systems. Since 2017, he has secured 20 grants funded by UKRI including EPSRC and Innovate UK, EU programmes such as Horizon 2020 and Horizon Europe, and industry, with a total project value exceeding £45 million. At Warwick, he established the Intelligent Control & Smart Energy (ICSE) research group consisting of around 20 PhD students and postdoctoral researchers, along with four state-of-the-art laboratories: the Offshore Renewable Energy Lab, the Renewable Energy Integration and Smart Grid Lab, the Hydrogen Technology Lab, and the Autonomous Systems Lab. His publication record features over 110 journal publications since 2020, including 'Heterogeneous multi-agent proximal policy optimization based control of solid oxide electrolysis and wind turbine generator for fast frequency response' in Applied Energy (2026), 'Spatiotemporal nonlinear ocean wave prediction with uncertainty quantification and universal predictable zone determination' in Applied Energy (2026), 'Flowformer: Toward a foundation model for full-flow-field wind farm wake modeling' in Renewable Energy (2025), and 'Wind farm control technologies: from classical control to reinforcement learning' (2022).