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, holding 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. Li earned her Ph.D. in Electrical and Computer Engineering from the National University of Singapore. Her research centers on data-driven approaches to evaluate the reliability and durability of photovoltaic systems and battery energy storage, utilizing knowledge graphs, neuro-symbolic AI, and AI reasoning to enhance renewable energy optimization.

Li's research interests include photovoltaic and storage performance and degradation, power outage detection, grid resilience, data-driven digital twins, satellite imagery analysis, knowledge graphs, and neuro-symbolic AI. She collaborates with the Center for Research in Computer Vision, the Department of Statistics and Data Science, UCF Coastal, and the School of Public Administration at UCF, as well as national laboratories such as the National Renewable Energy Laboratory and Sandia National Laboratories, universities including Arizona State University and Case Western Reserve University, and industrial partners comprising utility companies and manufacturers. Key publications feature 'Automated defect detection and localization in photovoltaic cells using semantic segmentation of electroluminescence images' (IEEE Journal of Photovoltaics, 2021), 'Impact of acetic acid exposure on metal contact degradation of different crystalline silicon solar cell technologies' (Solar Energy Materials and Solar Cells, 2023), 'Review of potential-induced degradation in bifacial photovoltaic modules' (Energy Technology, 2023), 'A comprehensive evaluation of contact recombination and contact resistivity losses in industrial silicon solar cells' (IEEE Journal of Photovoltaics, 2020), and 'Generalized deep learning model for photovoltaic module segmentation from satellite and aerial imagery' (Solar Energy, 2024). Her work contributes significantly to advancements in photovoltaic reliability modeling and energy systems simulation within engineering.