Peter Harrington
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolPeter Harrington is a machine learning research practitioner with seven years' experience applying deep learning and computer vision to problems in the physical sciences, drawing on a strong physics and astrophysics background. He has worked at national labs and HPC centers—Berkeley Lab and NERSC—helping bridge high-performance computing and AI workflows, and most recently held a Scientific ML role at NVIDIA focused on Earth-scale modeling. His training in computational modeling and magnetohydrodynamics gives him a practical edge in turning domain equations into efficient, scalable code. Peter is comfortable moving models from research prototypes to HPC-optimized deployments and has a track record of tackling parallel I/O and simulation performance bottlenecks. Colleagues rely on him for making complex scientific problems approachable through ML, and he brings a curiosity for astronomical-scale phenomena that informs creative modeling choices. Based in San Francisco, he combines academic rigor with production-minded engineering across science ML stacks.
7 years of coding experience
8 years of employment as a software developer
University of California Santa Cruz
Github Skills (38)
pytorch10
python10
machine-learning10
weather10
numpy10
deep-learning10
physics10
gpu10
neural-network10
climate-science9
acceleration9
autograd9
tensorflow9
tensor9
gpu-acceleration9
Programming languages (6)
Github contributions (5)
NERSC/sc21-dl-tutorial
Aug 2020 - Mar 2022
Material for the SC21 Deep Learning at Scale Tutorial
Contributions:4 reviews, 41 commits, 9 PRs in 1 year 6 months
deep-learningat-scalescalesc21
exalearn/epiCorvid
May 2020 - Jan 2021
Contributions:6 commits, 6 pushes in 8 months
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