معرفی
Berry Wen is an Assistant Professor at the University of Florida, specializing in Earth System Science and Sustainability. Her research integrates advanced machine learning (ML) with remote sensing technologies to improve precipitation estimation and monitor natural hazards. She holds a Ph.D. in Meteorology from the University of Oklahoma (2015), an M.S. in Geoinformatics (2012), and a B.S. in Ecology from East China Normal University (2006).
Her research focuses on three core areas: validation of remote sensing products, synergy between ground radar and satellite data, and applications of remote sensing in natural hazard monitoring. She has led multiple funded projects, including NASA's Ground Validation for the Atmospheric Infrared Sounder (AIRS) and NOAA/NSSL studies on radar interference impacts. Her work emphasizes high-quality training datasets for ML in Earth sciences and addresses the 'black box' challenge through variable impact analysis.
Recent studies include developing interpretable ML models for precipitation retrievals and advancing flood prediction through radar and satellite fusion. She actively collaborates with NASA JPL and the University of Oklahoma, contributing to initiatives like the RaXPol radar community instrument facility. Her research aims to bridge environmental science and societal resilience against extreme weather events.
Current projects include flash drought monitoring, AI-driven tornado prediction, and equitable flood risk assessment for Native American communities. She is actively recruiting graduate students interested in Earth system science and ML applications.
