Summary
Saeed Moghimi is a Ph.D.-trained coastal oceanographer and engineering leader with over a decade of experience building operational Earth system and storm surge forecasting systems for NOAA. As NOS Storm Surge Modeling Team Lead he scaled a team from 4 to 30+, led migration of legacy models to cloud and HPC platforms, and pioneered AI/ML-driven bias correction and probabilistic surge methods that materially improved forecast accuracy. He integrates hydrology, atmosphere, waves, and sediment dynamics into coupled operational models and has driven CI/CD, DevOps, and Agile practices across scientific teams. Saeed’s background spans academic research, international fellowships, and large-scale engineering projects, giving him rare expertise at the intersection of high-performance scientific computing, operational forecasting, and coastal resilience planning. Notably, he directed the first large-scale application of Neural Operator deep learning for surge bias reduction within NOAA’s operational pipeline.
10 years of coding experience
21 years of employment as a software developer
Fellowship, physical oceanography, Fellowship, physical oceanography at Leibniz Institute for Baltic Sea Research, Rostock, Germany
Doctor of Philosophy (PhD), Civil Engineering - hydraulics, Doctor of Philosophy (PhD), Civil Engineering - hydraulics at Tarbiat Modares University
Bachelor of Science (BSc), Civil Engineering, Bachelor of Science (BSc), Civil Engineering at Isfahan University of Technology
English, German, Persian