Jianli Gao

Dr. Jianli Gao is a Researcher in the Control and Power (CAP) Research Group at Imperial College London’s Department of Electrical and Electronic Engineering, within the Faculty of Engineering. His work focuses on modeling and control of future power grids dominated by renewable energy sources, supported by a National Grid ESO-funded project. He is affiliated with the Electric Power Innovation for a Carbon-free Society (EPICS) Center and the KIOS Research and Innovation Center of Excellence.

Dr. Gao holds a PhD in Electrical and Electronic Engineering from Imperial College London (2024), with a thesis on power systems stability under Prof. Alessandro Astolfi and Dr. Balarko Chaudhuri. He also earned a First Class B.Eng. from the University of Manchester (2016–2018) and a B.Eng. from North China Electric Power University (2014–2016), alongside an M.Sc. in Control Systems (Distinction) from Imperial (2018–2019).

His research interests span power systems stability, control theory, renewable energy integration, dynamical systems, and machine learning applications. His recent work includes developing graph neural networks for stock market prediction and multi-scale analysis, alongside contributions to neuroscience data repositories like the developing Human Connectome Project (dHCP).

Dr. Gao’s publications reflect a blend of foundational engineering and cross-disciplinary innovation, including projects on prosthetic interface optimization and cloud-based metabolomics tools. His work bridges theoretical advancements in control systems with practical applications in energy, finance, and healthcare.