Ethan Rooke is a postdoctoral scholar and applied mathematician with seven years of experience translating topological data analysis into neuroscience applications. Based at the University of Iowa, he holds a PhD in Applied Mathematics and has a strong teaching background from six years as a university TA. Ethan contributes to open-source tools like scikit-tda’s Kepler Mapper, improving Mapper algorithm stability and modernizing examples and benchmarks. He combines rigorous theoretical training with practical software fixes—such as addressing numerical edge cases and reproducibility via random states—making complex TDA workflows more robust for neuroscience research.
7 years of coding experience
Bachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at University of California, Riverside
Doctor of Philosophy - PhD, Applied Mathematics, Doctor of Philosophy - PhD, Applied Mathematics at University of Iowa
Kepler Mapper: A flexible Python implementation of the Mapper algorithm.
Role in this project:
Data Scientist
Contributions:5 commits, 2 PRs, 1 comment in 4 months
Contributions summary:Ethan primarily focused on updating the Kepler Mapper library's functionality. They updated the examples and benchmarks to use the new cover API, replacing the older `nr_cubes` argument. Additionally, the user addressed several issues, including fixing a divide-by-zero error, deprecation warnings, and future warnings related to NumPy, and added a random state to AffinityPropogation, increasing the stability of the models. These changes are focused on the implementation details of the Mapper algorithm.
Contributions:18 pushes, 84 branches in 4 years 6 months
nixosnix-packages
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Ethan Rooke - Postdoctoral Scholar at University of Iowa