Alexander Bogatskiy
Postdoctoral Fellow at Simons Foundation
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolAlexander Bogatskiy is a postdoctoral fellow and research scientist with 11 years of experience developing physics-informed machine learning architectures for frontier scientific problems. Based at the Flatiron Institute and affiliated with the UChicago Data Science Institute, he specializes in covariant neural networks informed by condensed matter theory and mathematical physics. He holds advanced degrees from the University of Chicago and Saint Petersburg State University and has a strong track record in teaching and outreach, from graduate instruction to extracurricular mathematics courses for youth. Comfortable bridging rigorous theory and practical computational tools, he has implemented domain-specific modeling code and translates deep mathematical ideas into ML architectures for scientific discovery. Outside research he is engaged in education, social philosophy and social justice, and is an avid cyclist—a perspective that often informs his collaborative and human-centered approach to complex problems.
10 years of coding experience
1 year of employment as a software developer
Master's Degree, Mathematical Physics, Master's Degree, Mathematical Physics at Saint Petersburg State UniversityPhysics, Mathematics, Physics, Mathematics at Phys-Math Lyceum 30Doctor of Philosophy (Ph.D.), Condensed Matter Theory, Doctor of Philosophy (Ph.D.), Condensed Matter Theory at The University of Chicago
English, Russian
Github Skills (61)
colorize10
autocomplete10
latex10
compile10
boost10
permutation9
typesetting9
jupyter-notebook8
python8
deep-learning8
javafx8
gpu8
machine-learning8
academia8
aggregator7
Programming languages (8)
Github contributions (5)
abogatskiy/PELICAN
Jun 2022 - Nov 2022
Contributions:267 commits, 26 PRs, 15 pushes in 5 months
covariantinvariantjuliapermutationaggregator
abogatskiy/Geometry-Autistic-Intro
May 2024 - Mar 2025
Contributions:6 releases, 99 pushes, 4 branches in 10 months
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