Reilly Raab
Data Scientist
United States
Summary
🤩
Rockstar🎓
Top SchoolReilly Raab is a data scientist and researcher who blends a strong physics and mathematical background with hands-on expertise in machine learning, optimization, and multi-agent systems. Currently at Pacific Northwest National Laboratory after a postdoc focused on agentic language model programs, Reilly has published at NeurIPS, ICLR, and AAAI, including a NeurIPS Spotlight Paper Award. Their work spans theoretical results—connecting replicator dynamics to natural gradient descent and deriving adversarial fairness bounds—to high-performance implementations using JAX and Taichi for GPU-accelerated multi-agent simulations. With prior experience automating embedded and IoT workflows in industry, Reilly pairs rigorous theory with practical systems engineering and a knack for turning complex, interacting-agent problems into scalable computational experiments.
1 year of coding experience
6 years of employment as a software developer
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at UC Santa BarbaraUniversity of California Santa Cruz
Github Skills (14)
constrained-optimization9
nonlinear-optimization8
dynamical-systems8
nonlinear-dynamics8
system-identification8
predictive8
control-systems6
model-predictive-control6
optimization5
parametric5
physics5
deep-learning5
pytorch5
differentiable-programming5
Programming languages (1)
Github contributions (1)
pnnl/neuromancer
Dec 2024 - Dec 2024
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Contributions:2 pushes, 1 branch in 1 day
nonlinear-optimizationdynamical-systemsproblemsmodel-predictive-controldifferentiable-programming
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