Ryan Johnson is an Assistant Professor of Civil and Environmental Engineering at the University of Utah who combines eight years of applied research experience with hands-on software and field engineering to improve hydrological and water-resources forecasting. He leverages open-source tools, abundant hydrological data, and machine learning—particularly deep neural networks and LSTMs—to tackle hard problems like heterogeneous snow distribution and extending the National Water Model to seasonal water-supply forecasts. Ryan also designs low-cost, low-power snow monitoring hardware to ground-truth models in key Western US catchments, bridging sensor development and data-driven modeling. His background spans academia and operational research roles that transitioned research code into reliable open-source projects and web-based data services, reflecting a practical focus on deployable science. Outside the lab he tests his models in the field—mountain biking and fly fishing in summer, and digging snow pits and skiing in winter—giving him uncommon literal exposure to the environments he models.
8 years of coding experience
13 years of employment as a software developer
The University of Utah
Associate of Science (AS), Environmental Science, Associate of Science (AS), Environmental Science at Saddleback College
Master of Science (M.S.), Environmental Science, 3.99, Master of Science (M.S.), Environmental Science, 3.99 at California State University-Fullerton
Bachelor's of Science, Earth System Sciences, 3.71, Bachelor's of Science, Earth System Sciences, 3.71 at University of California, Irvine
The evolution of the Snow Water Equivalent Machine Learning modeling workflow - flexible spatial resolution, flexible model regions, and flexible grid/mesh
Contributions:1 review, 60 PRs, 39 pushes in 1 year 8 months
machine-learning
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