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Jeremy Diaz is a machine learning specialist whose strives to provide more accurate, extensive, and timely estimates of U.S. water resources
Jeremy Diaz has academic backgrounds in ecology, geography, applied mathematics, and electrical engineering. His expertise and interests lie in geographic and quantitative methods such as GIS, remote sensing, statistics, image processing, natural language processing, high performance computing, cloud computing, and machine/deep learning with an emphasis on advanced neural networks. In prior research, he has applied these methods to improve natural hazard response and impact estimation as well as segmenting and forecasting satellite imagery with regard to flooding, wildfires, tornadoes, and volcanoes.
At USGS, Jeremy’s work is concerned with the research and development of modeling to improve the accuracy of existing estimates, providing consistent estimates more densely across the country, and delivering water estimates to public-facing reports and websites. Applications areas include streamflow drought, stream temperature, water use, and lake ice.
Snapshot of modeled stream temperature data from across the lower 48 United States on May 25, 2020. Data from https://doi.org/10.1016/j.envsoft.2025.106655
Snapshot of modeled stream temperature data from across the lower 48 United States on May 25, 2020. Data from https://doi.org/10.1016/j.envsoft.2025.106655
Snapshot of modeled stream temperature data from across the lower 48 United States on May 25, 2020. Data from https://doi.org/10.1016/j.envsoft.2025.106655
Snapshot of modeled stream temperature data from across the lower 48 United States on May 25, 2020. Data from https://doi.org/10.1016/j.envsoft.2025.106655