Selected Work
A sample of recent projects across geospatial data science, applied machine learning, and environmental research.
Developed computational workflows to process 100 years of spatial climate data, enabling identification of climate analogs to support land management and species forecasting under future climate scenarios.
Built reproducible land cover maps for all 48 contiguous U.S. states using USDA HPC infrastructure, integrating remote sensing, vegetation, and administrative data. Published in Scientific Data (2024).
Integrated land cover, remote sensing, and vegetation data to estimate floral resources for bees across 496 U.S. counties, supporting pollinator conservation planning.
Developed a public-facing R Shiny application mapping insect life stages in Oregon, including full integration with an automated backend workflow hosted on Amazon Web Services.
Engineered spatiotemporal features and built ML models predicting weather-related power distribution failures, with production pipelines generating outage risk predictions at hourly intervals for three electric utility clients.
Synthesized and evaluated 170+ technical tools for assessing conservation practices, resulting in four high-level graphics and strategic investment recommendations delivered to a federal agency client.