Evan Anders is an AI researcher and Member of Technical Staff with a decade of experience bridging computational science and software engineering. He holds a Ph.D. in Astrophysical and Planetary Sciences and spent nine years researching astrophysical fluid dynamics—work that included simulating stellar “twinkle” and contributing to high-impact science coverage. Evan is an active contributor to the Dedalus project, improving core spectral-PDE functionality like basis functions, operators, and CFL calculations, reflecting deep expertise in numerical methods and robust testing. Now at Anthropic, he applies his background in physics-scale simulation and scientific software to AI research, while off-duty he pursues climbing, hiking, and knitting—an unusual mix that hints at patience and precision in both code and craft.
10 years of coding experience
Doctor of Philosophy (Ph.D.), Astrophysical and Planetary Sciences, Doctor of Philosophy (Ph.D.), Astrophysical and Planetary Sciences at University of Colorado Boulder
Bachelor of Science (BS), Physics, Bachelor of Science (BS), Physics at Whitworth University
A flexible framework for solving PDEs with modern spectral methods.
Role in this project:
Back-end Developer
Contributions:9 reviews, 13 commits, 5 PRs in 6 years 8 months
Contributions summary:Evan primarily contributed to bug fixes and improvements within the Dedalus framework, focusing on the core functionalities related to basis functions, operators, and CFL calculations. Their commits involved fixing issues in the `BallBasis` and `SphericalGradient` classes, as well as restructuring the CFL implementation. They also added testing to verify their changes. These changes suggest a focus on maintaining and improving the core functionality of the Dedalus framework.
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