I am a computational physicist specializing in numerical and statistical methods, applied to high-resolution large-eddy simulation (LES) models of the atmosphere. I am particularly interested in implementing and optimizing parallel processing of large, multi-dimensional data, as well as machine learning algorithms such as Gaussian Process (GP) and deep neural networks to better represent the complex dynamics seen in the modelled data.
Skills¶
15+ years of project experience with
Python,C, andC++Parallel processing of multi-dimensional data with
C++(xtensor) andJuliaStatistical analysis with
Numpy,ScipyandScikit-learnData pre-processing with
Pandas/PolarsandArrow/ParquetMachine learning experience with
Pytorch[1],KerasandJaxExperience with RDBMS with
MongoDB,PostgresSQLandMariaDB
Education¶
| Ph.D. in Atmospheric Science | 2014 - 2024 |
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| University of British Columbia Vancouver, BC, Canada |
| M.Sc. in Atmospheric Science | 2012 - 2014 |
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| McGill University Montreal, Quebec, Canada |
| B.Sc. Combined Honours in Computer Science and Physics | 2008 - 2012 |
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| University of British Columbia Vancouver, BC, Canada |
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Employment¶
| Research Scientist | 2020 - 2023 |
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| Korea Polar Research Institute (KOPRI) Incheon, Korea |
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Publication¶
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See this Jupyter Book, which is a collection of Jupyter notebooks accompanying Oh and Austin, 2015.
- Oh, G., & Austin, P. H. (2024). Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression. 10.5194/egusphere-2024-352