Oleksandr Pavlyk
Senior Software Engineer at NVIDIA
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolOleksandr Pavlyk is a senior software engineer with nine years of professional experience and a deep research background (PhD-level physics) in computational mathematics, now working on CUDA-Python at NVIDIA from Austin. He specializes in numerical evaluation of special functions, symbolic manipulation, probability and random number generation, and cross-platform C/C++ and Mathematica tooling, having built core kernel features at Wolfram earlier in his career. At Intel he focused on enabling high-performance Python via MKL/DAAL and accelerated ML workflows, and his open-source contributions span high-impact projects like NumPy, SciPy, pybind11, Cython, and scikit-learn—improving performance, correctness, and interoperability. He has a proven track record of squeezing performance out of scientific code (e.g., tomopy gridrec optimizations and FFT/MKL integration) and adding robust serialization and integration features for ML tooling. A proud Ukrainian, he combines academic rigor with practical engineering to bridge symbolic math, numerical algorithms, and production-grade performance engineering.
9 years of coding experience
20 years of employment as a software developer
Ph.D. Physics, Ph.D. Physics at Penn State University
English, Ukrainian, Russian, Italian
Stackoverflow
1reputation
0reached
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Github Skills (41)
algorithm10
fftw10
code-optimization10
image-reconstruction10
algorithms10
scipy10
c-language10
tomography10
pytest10
python10
testing10
scikit10
memory-management10
fortran10
machine-learning10
Programming languages (18)
Github contributions (5)
uxlfoundation/scikit-learn-intelex
Sep 2018 - May 2020
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:3 reviews, 183 commits, 84 PRs in 1 year 8 months
Contributions summary:Oleksandr primarily contributed to extending the Scikit-learn Intelex library to support pickling for model and result classes, enabling the serialization and deserialization of models. They refactored internal build processes, renaming internal functions and merging code related to pickling. Additionally, the user worked on integrating decision tree functionality, potentially for improved performance and efficiency using Intel DAAL. Their contributions are related to ML and the performance optimization of an ML framework.
pythonswrepoai-machine-learningdata-scienceintel
numpy/numpy
Aug 2016 - Jan 2023
The fundamental package for scientific computing with Python.
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:2 reviews, 26 commits, 17 PRs in 6 years 5 months
Contributions summary:Oleksandr primarily contributed to the NumPy project by enhancing the test suite. Their commits included adding new tests for specific functionalities, such as FFT transforms with axes and ensuring the correctness of ufuncs with non-contiguous arrays. Furthermore, the user addressed build failures on specific platforms, demonstrating a focus on ensuring cross-platform test coverage and project stability. These contributions improved the reliability and robustness of the NumPy library.
lapackpythonmpindarrayconvolution
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