David Gagne
Machine Learning Scientist at NCAR - The National Center for Atmospheric Research
Boulder, Colorado, United States
Summary
👤
Senior🎓
Top SchoolDavid Gagne is a Machine Learning Scientist and head of the MILES group at NCAR with 11 years of experience applying deep learning to atmospheric science and operational forecasting. He holds a Ph.D. in Meteorology from the University of Oklahoma and has progressed from undergraduate research on storm identification to leading research on ML-driven severe weather forecasting, model parameterization, and renewable energy prediction systems. At NCAR he builds and integrates ML models into earth system workflows, translating research prototypes into tools that improve probabilistic and gridded forecasts. Known for bridging domain expertise in meteorology with practical ML engineering, he has a track record of turning complex physical problems into data-driven solutions used by operational communities. Based in Boulder, he combines academic rigor with hands-on development experience spanning internships at NOAA to postdoctoral and staff scientist roles.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Meteorology, Doctor of Philosophy (Ph.D.), Meteorology at University of Oklahoma
English
Github Skills (135)
hail10
python10
data-manipulation10
forge10
geojson10
geodesy10
dataframes10
pandas10
hazard10
noaa10
risk-assessment10
storm10
data-science10
zarr10
verification10
Programming languages (8)
Github contributions (5)
NCAR/bridgescaler
Dec 2022 - Sep 2024
Bridge your scikit-learn scaler parameters between Python sessions and users. Distribute your scaling across multiple processes and data subsets.
Contributions:8 releases, 3 reviews, 24 PRs in 1 year 9 months
aimachine-learningscikit-learn
ai2es/ptype-physical
Mar 2022 - Jun 2024
Contributions:24 reviews, 22 PRs, 60 pushes in 2 years 3 months
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