Jared Smith
Machine Learning Specialist at U.S. Geological Survey (USGS)
Reston, Virginia, United States
Summary
👤
Senior🎓
Top SchoolJared Smith is a Machine Learning Specialist at the U.S. Geological Survey with a decade of experience applying statistical and spatial-data methods to environmental and water-resources challenges. Trained with a PhD from Cornell, he blends Bayesian inference, global sensitivity analysis, and stochastic optimization with process-guided deep learning to produce robust, decision-relevant models for flood prediction, watershed management, and geothermal resource assessment. His work spans practical applications—optimizing green infrastructure placement under uncertainty in the Chesapeake Bay watershed—to probabilistic geothermal playfairway analyses for direct-use heating across the Appalachian Basin. Comfortable bridging academic research and government practice, he often couples physical process models with modern ML to improve interpretability and resilience of predictions. An uncommon strength is his track record of turning geostatistical uncertainty quantification into actionable site-selection and techno-economic insights for energy and water systems.
10 years of coding experience
9 years of employment as a software developer
Research Exchange Student, Research Exchange Student at Iceland School of Energy - at Reykjavik UniversityExchange Student, Exchange Student at University of NewcastleBachelor's Degree, Bachelor's Degree at Clarkson UniversityDoctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at Cornell UniversityHigh School, High School at Islip High School
Stackoverflow
280reputation
33kreached
11answers
2questions
Github Skills (67)
drb10
regression9
bootstrap9
frequency-analysis9
hydrology-statistical9
region9
logistic-regression9
water8
prediction8
traits8
hydrology7
ord7
systems-biology7
r-package7
machine-learning-models7
Programming languages (5)
Github contributions (5)
jds485/regional-hydrologic-forcings-ml
Nov 2021 - May 2023
Repo for machine learning models for regional prediction of hydrologic forcing functions. FY22 regions: Delaware River Basin (DRB) region, and Upper Colorado River Basin (UCOL) region.
Contributions:4 PRs, 169 pushes, 33 branches in 1 year 5 months
predictionriverdrbregionsmachine-learning
USGS-R/drb-inland-salinity-ml
Oct 2021 - Nov 2022
Code repo for Delaware River Basin machine learning models that predict inland salinity.
Contributions:280 reviews, 363 commits, 87 PRs in 1 year 1 month
machine-learning-models
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