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Wonei Choi is a leading researcher in satellite-based aerosol remote sensing, specializing in the application of machine learning to atmospheric classification. Her work centers on developing innovative algorithms to identify and categorize aerosol types—such as dust, smoke, and urban pollution—using space-borne measurements. Choi’s major contributions include pioneering the use of random forest models trained on AERONET observational data to overcome the limitations of traditional satellite retrieval methods. Her 2021 study, “A First Approach to Aerosol Classification Using Space-Borne Measurement Data,” has garnered 26 citations and established a foundational machine-learning framework for global aerosol typing. She further advanced this methodology in subsequent works, achieving 19 and 9 citations respectively, by expanding spatial coverage and applying the model to Asian capital cities. These achievements have significantly improved the accuracy and reach of aerosol classification, enabling better monitoring of air quality and climate impacts. Choi’s research is notable for its practical integration of ground-based and satellite data, offering a scalable solution for environmental monitoring. Her work is essential reading for students and researchers in atmospheric science, remote sensing, and machine learning applications.