Alexander Andreev
Software Engineer
Dublin, Dublin 1, Ireland
Summary
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RockstarAlexander Andreev is a software engineer based in Dublin with 8 years of experience specializing in machine learning performance engineering. He focuses on optimizing core ML libraries, contributing significant performance and correctness improvements to Intel oneAPI projects such as scikit-learn-intelex and oneDAL. His work includes accelerating scikit-learn primitives like train_test_split, adding kNN support via daal4py, and fixing memory leaks and gradient-boosted tree implementations for faster, reliable training. Comfortable in back-end systems and low-level algorithmic optimization, he brings a practical blend of profiling-driven fixes and test-suite-backed feature integration. Colleagues rely on him to squeeze production performance from ML pipelines while keeping implementations robust and maintainable. An understated strength is his habit of pairing performance wins with comprehensive tests, reducing regressions in high-impact open-source components.
8 years of coding experience
Github Skills (11)
data-analysis10
scikit10
machine-learning10
oneapi10
c-language10
machine-learning-algorithms10
cpp10
cprogramming-language10
python10
scikit-learn10
testing8
Programming languages (6)
Github contributions (5)
uxlfoundation/scikit-learn-intelex
Jun 2020 - Jan 2023
Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 685 reviews, 53 commits in 2 years 7 months
Contributions summary:Alexander primarily contributed to optimizing and extending the `scikit-learn-intelex` library, focusing on accelerating scikit-learn algorithms with Intel's oneAPI. Their work included optimizing the `train_test_split` function, adding support for k-Nearest Neighbors (kNN) algorithms with daal4py, and addressing memory leak issues. Furthermore, the user integrated and tested new features like kNN and provided a test suite to ensure the correct functionality and performance.
pythonswrepoai-machine-learningdata-scienceintel
uxlfoundation/oneDAL
Nov 2019 - Jan 2023
oneAPI Data Analytics Library (oneDAL)
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 releases, 321 reviews, 70 commits in 3 years 2 months
Contributions summary:Alexander's commits primarily focus on optimizing and correcting code related to Gradient Boosted Trees (GBT) within the oneAPI Data Analytics Library (oneDAL). The contributions involve performance improvements and bug fixes, specifically targeting the implementation of GBT algorithms. The changes include modifications to training tree builders, and gradient and hessian calculations, indicating a focus on enhancing the efficiency of machine learning algorithms. These optimizations also extended to the regression and classification implementations.
swrepodata-analyticsanalyticscppdata-analysis
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