Summary
Isaac Gerg is an AI Applied Scientist with over a decade of hands-on experience marrying first-principles mathematical analysis to high-performance ML engineering, focused on remote sensing, signal processing, and robustness. He has led DARPA and Navy programs, delivered production-grade systems like the ASASIN real-time SAS image-formation code, and published 50+ papers exploring physics-centric and biologically inspired approaches to make learning more efficient and robust. Comfortable across C++/CUDA, JAX/PyTorch, and large-scale HPC pipelines, he accelerates research velocity while reducing labeled-data needs—achieving 90% label reduction and dramatic forecasting improvements in operational settings. Isaac gravitates to “coalface” problems where theory breaks and operational constraints matter, translating prototypes into funded, deployed capabilities for defense, climate, and commercial customers. He completed a PhD in four years while working full time, blends interpretability and synthetic-data expertise, and actively advises on technical requirements for high-consequence users such as DoD customers.
11 years of coding experience
19 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical Engineering, Doctor of Philosophy - PhD, Electrical Engineering at Penn State University
Bachelor of Science - BS, Computer Engineering (Honors), Bachelor of Science - BS, Computer Engineering (Honors) at Schreyer Honors College at Penn State