C. Braxton Owens

Confidence without doubt is the fastest path to being dangerously wrong. - me

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Currently, I am a PhD student at BYU working in the Biophysics group. I have the privilege of working with wonderful scientists like Grant Jensen, Bryan Morse, and Gus Hart on a daily basis. I love linear algebra and its applications, I live in the terminal with neovim, and my favorite textbook is High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications, which I just call “High-Dim, Low-Dim.”

I am a believer in open collaboration and am grateful for the community of open-source developers who make my life better.

I am currently conducting research in computational cryo-ET, using both classical and deep ML methods to identify low-signal macromolecular complexes in tomograms. This research allows us to achieve the near-atomic scale resolution needed to understand fundamental life processes.

On a more personal note, I love to read fantasy, snowboard, and teach my three sons that it was actually Leibniz, not Newton, who invented calculus.

news

Oct 19, 2026 Our paper “A Dataset and Competition to Automatically Find Flagellar Motors” was accepted to Communications Biology! 🎉
Oct 15, 2026 Our paper “POPSICLE: Benchmark Datasets for Segmentation and Localization in CryoET” was accepted to WACV 2027!
Sep 01, 2026 Wrapped up a summer internship as a Machine Learning Engineer Intern at Adobe in Lehi, Utah.
Sep 03, 2024 Website created!!

latest posts

selected publications

  1. Feature Engineering Descriptors, Transforms, and Machine Learning for Grain Boundaries and Variable-Sized Atom Clusters
    C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson, and 5 more authors
    npj Computational Materials, Jan 2025
  2. A Dataset and Competition to Automatically Find Flagellar Motors
    C. Braxton Owens, Rachel Webb, T. J. Hart, and 8 more authors
    Communications Biology, Oct 2026
    In press