Wei Ling received his MS in computer engineering from the University of Southern California (USC) and is currently a PhD student in the chemical engineering department. He is a member of the Subsurface Energy and Environmental Systems (SEES) lab at USC, which focuses on integrating state-of-the-art data science and machine learning techniques with physical insights to develop efficient fit-for-purpose predictive tools for energy and environmental systems. His research interests include automation and control of geothermal power plants using deep learning techniques. His current work is focused on learning-based model predictive control to improve power plant performance and sustainability.