Alex Rogozhnikov
AI Scientist, Protein Design at Chai Discovery
San Carlos, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Rogozhnikov is an AI scientist specializing in protein design with 11 years of experience, currently at Chai Discovery in San Carlos, CA. He combines a PhD in physics and mathematics with extensive ML practice applied across domains—from particle physics at Yandex (authoring hep_ml and contributing to yandex/rep) to on-device vision and speech at Samsung and translational biotech leadership at Herophilus and Parallel Bio. A prolific open-source contributor, Alex is a core contributor to einops and focuses on making scientific ML tooling more usable and reliable. He’s known for translating rigorous quantitative methods into practical pipelines for phenotyping, drug screening, and protein design, often bringing physics-flavored perspectives to biological variability and model interpretability.
12 years of coding experience
9 years of employment as a software developer
Master's degree, Theoretical Physics, Master's degree, Theoretical Physics at Higher School of EconomicsMaster's degree, Machine learning & Data Science, Master's degree, Machine learning & Data Science at Yandex School of Data AnalysisLomonosov Moscow State University
Russian, English
Github Skills (11)
xgboost10
machine-learning10
pytorch10
tensor10
documentations10
documentation10
numpy9
tensorflow9
jax8
tensorflow27
flax6
Programming languages (18)
Github contributions (5)
arogozhnikov/einops
Sep 2018 - Jan 2023
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Role in this project:
ML Engineer
ML EngineerContributions:21 releases, 14 reviews, 501 commits in 4 years 4 months
Contributions summary:Alex appears to be an ML Engineer, primarily focused on developing and refining the `einops` library for tensor manipulation. They are implementing and testing new functionalities for tensor operations, including reshaping and applying reductions, to be used in deep learning models. Their contributions involve modifications to the core `einops.py` file, and adding new tests and documentation in `tests.py`. The commits show that the user is adding support for features like support for grouping, oneflow support and code for testing.
pytorchdeep-learningnumpycupytensor
yandex/rep
Apr 2015 - Nov 2016
Machine Learning toolbox for Humans
Role in this project:Data Scientist
Data ScientistContributions:374 commits, 25 PRs, 256 pushes in 1 year 7 months
Contributions summary:Alex's commits primarily focused on modifying docstrings and parameter definitions within the "rep" repository, specifically concerning the "xgboost.py" and "tmva.py" files. These modifications suggest a focus on clarifying and enhancing the documentation for machine-learning models within the library. The changes included improving the clarity of descriptions for parameters like 'n_estimators', indicating a user involved in improving the usability and understanding of machine learning tools for humans.
pythondata-sciencetoolboxmachine-learningclustering
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