Evan Thomas is a mathematical biologist and software engineer with nearly a decade of experience bridging computational neuroscience and large-scale data management. He develops and applies advanced parameter-handling methods and real-time experimental systems—such as dynamic voltage clamp platforms—to link ion channel biophysics to network-level phenomena like epilepsy, and has generated experimentally validated predictions for intestinal motor circuits. Evan has also built production-grade, high-performance software at Arcitecta for managing trillions of objects and multi-petabyte datasets, and contributed backend and batch-system improvements to the widely used Toil workflow engine. Comfortable across academia and enterprise, he combines deep theoretical training (PhD in computational neuroscience, MA in theoretical physics) with hands-on systems and DevOps expertise, enabling reproducible, scalable science and robust data infrastructure.
9 years of coding experience
24 years of employment as a software developer
MA, theoretical physics, MA, theoretical physics at State University of New York at Stony Brook
The University of Melbourne
MAppSci(IT), Knowledge based systems, I received a High Distinction for 8/9 courses, MAppSci(IT), Knowledge based systems, I received a High Distinction for 8/9 courses at RMIT University
A scalable, efficient, cross-platform (Linux/macOS) and easy-to-use workflow engine in pure Python.
Role in this project:
Backend & DevOps Engineer
Contributions:25 commits, 5 PRs, 56 comments in 10 months
Contributions summary:Evan primarily contributed to the backend infrastructure and batch system integration within the Toil project. They decoupled batch systems from the core codebase, fixed issues with environment variable passing to the Slurm batch system, and improved its error handling. The user also updated the Mesos batch system and implemented changes in the options and abstract batch system modules, demonstrating skills in system design and batch system management.
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