Rate My Professor Mengjie Li

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.