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Geoffrey is a Principal AI Scientist with a background in astrophysics and high-performance computing. He has spent his entire career facilitating researchers on TOP500 supercomputers — across campus research computing centers and at Department of Energy national laboratories.

His work sits at the intersection of systems engineering and scientific computing: building the infrastructure, tooling, and software that enables large-scale research. From distributed task execution to AI-assisted workflows, Geoffrey specializes in making complex systems accessible and efficient.

Expertise

Astrophysics

Stellar classification, spectroscopic analysis, and data-intensive astronomical surveys.

Research Software Engineering

Production-grade tools and libraries for scientific computing — from CLI frameworks to distributed systems.

High-Performance Computing

Large-scale cluster management, job scheduling, and parallel execution across thousands of nodes.

Artificial Intelligence

Applied AI and machine learning for scientific computing and research automation.

Research Workflows

Scalable task execution and pipeline orchestration for reproducible, large-scale research on shared infrastructure.

Citizen Science

Independent computational projects for fun and discovery — including from-scratch Mersenne prime search tooling.

Selected Projects

  • HyperShell — Scalable, cross-platform task execution utility for embarrassingly parallel workloads on HPC
  • CmdKit — A command-line toolkit for building Python applications with a clean, composable architecture

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