J Johnson is a data engineer and scientific machine learning specialist with 11 years of experience applying ML to climate and Earth-observation problems, currently supporting UNEP’s Methane Alert and Response System in Madrid. He integrates and operationalizes diverse satellite sources, adapts databases and pipelines for automated alerts, and deploys AI models to improve methane emissions detection at scale. His background includes postdoctoral work developing geographically weighted Gaussian process methods for extreme event attribution and building differentiable physics and surrogate emulators for ocean and satellite data. Comfortable spanning research and production, he combines rigorous uncertainty quantification (NumPyro/Jax) with MLOps practices (PyTorch, PyTorch-Lightning, W&B) to turn scientific models into operational systems. An unexpected strength is his track record of accelerating expensive simulations via learned surrogates, delivering research-grade accuracy with substantial computational speed-ups.
11 years of coding experience
9 years of employment as a software developer
Master of Science (MS), Applied and Computational Mathematics, Master of Science (MS), Applied and Computational Mathematics at Rochester Institute of Technology
History, Scientific and Technical Communication, History, Scientific and Technical Communication at University of Oxford
Bachelor of Science (BS), Mathematical Science, Bachelor of Science (BS), Mathematical Science at Florida Institute of Technology
Doctor of Philosophy (Ph.D.), Electrical Engineering, Doctor of Philosophy (Ph.D.), Electrical Engineering at University of Valencia
High School / International Baccalaureate Diploma, High School / International Baccalaureate Diploma at Emirates International School
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