Haejun Oh is a Senior Data Scientist with a PhD in Physics and six years of experience applying ML/AI to both high-energy neutrino research and enterprise middle-office automation. He developed a Sparse Convolutional Neural Network that cut training wall-time by 10% and memory use by 84% while preserving accuracy, and used advanced covariance and likelihood methods to probe sterile neutrino hypotheses for NOvA. Now at JPMorgan Chase, he translates research-grade modeling and large unstructured data handling into practical improvements in operational efficiency. Comfortable bridging academia and industry, he brings deep statistical rigor, efficient model design, and hands-on engineering to production problems.
6 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at University of Cincinnati
Burr & Burton Academy
Bachelor of Science (BS) Physics, Bachelor of Science (BS) Physics at University of Rochester
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