Advancing responsible geospatial AI to understand and mitigate coupled nature-human risks across scales.
Developing explainable GeoAI models to assess wildfire risk and identify socially vulnerable communities in the western U.S. We integrate multi-source remote sensing, socioeconomic data, and physics-informed AI for near-real-time risk mapping.
Learn MoreDeep learning models for automated flood damage assessment using Sentinel-1 SAR and Sentinel-2 optical imagery, with bias mitigation across socioeconomic regions.
Investigating how coupled nature-human system dynamics influence hurricane impact severity and recovery trajectories across Gulf Coast communities.
Foundational research examining sources of geographic and demographic bias in GeoAI models, developing methodological frameworks for fairness-aware geospatial AI.
PLACEHOLDER: Mapping urban heat island patterns with high spatial resolution and analyzing their unequal distribution across income and race demographics in U.S. cities.
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Developed a comprehensive multi-hazard risk atlas integrating floods, tornadoes, severe storms, and drought for the Tennessee Valley region to support emergency management planning.
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PLACEHOLDER: A curated list of key publications will appear here. See Google Scholar for the full list.
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Interested in research collaboration or joining the GRIND Lab? We welcome inquiries from prospective students, postdocs, and collaborators.