Rate My Professor Mengjie Li

Mengjie Li is an assistant professor in the School of Modeling, Simulation, and Training at the University of Central Florida. She holds secondary joint appointments in the Department of Computer Science, the Department of Materials Science and Engineering, and the Department of Electrical and Computer Engineering. She is also an affiliated member of the Resilient, Intelligent, and Sustainable Energy Systems cluster and the Florida Solar Energy Center. At UCF, she actively collaborates with the Center for Research in Computer Vision, the Department of Statistics and Data Science, UCF Coastal, and the School of Public Administration. Beyond UCF, she engages in partnerships with national laboratories, including the National Renewable Energy Laboratory and Sandia National Laboratories; universities such as Arizona State University and Case Western Reserve University; and various industrial partners, including utility companies and manufacturers. She earned her Ph.D. in Electrical and Computer Engineering from the National University of Singapore, completing her doctoral studies from September 2013 to January 2018. Since June 2022, she has served in her current roles at UCF.

Her research focuses on data-driven approaches to understand the reliability and durability of photovoltaic and battery energy storage systems. Her work includes the deployment of knowledge graphs, neuro-symbolic AI, and AI reasoning to optimize energy generation from renewable energy resources. Specific research interests encompass photovoltaic and storage performance and degradation, power outage detection, grid resilience, data-driven digital twins, satellite imagery analysis, knowledge graphs, and neuro-symbolic AI. Key publications include Review of Potential-Induced Degradation in Bifacial PV Modules (Energy Technology, 2022), Materials Data Science Ontology (MDS-Onto) (Scientific Data, 2025), and Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries (Frontiers in Energy Research, 2023).