I am a PhD Candidate with the Spatial Sciences Institute at USC. I obtained my Bachelor's degree in Geographic Information Science from Sun Yat-Sen University and was a Research Assistant studying artificial light at night at The University of Hong Kong.
My research develops new techniques for analyzing Earth Observation data to measure progress on the Sustainable Development Goals (SDGs). My work so far covers mapping of crop types, urbanization, air pollution, artificial light at night, and more recently impacts of temperature variations on human health. My latest research published in PNAS Nexus analyzed the disparities in exposure to daily temperature variation across US racial/ethnic and income groups.
I develop machine learning models to analyze remote sensing data [AI for Earth]. These machine learning models include mapping with unknown classes [open-set recognition], image classification with limited resources or samples [multitask learning, active learning, semi-supervised learning, ensemble learning], and temperature data reconstruction with uncertainty estimates [Bayesian deep learning].