Alex Belopolsky
Sr. Software Engineer at IHS Markit
Ann Arbor, Michigan, United States
Summary
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Rockstar🎓
Top SchoolAlex Belopolsky is a Senior Software Engineer based in Ann Arbor with 20 years of experience building robust back-end systems and improving developer-facing tools. He has deep expertise in core Python internals and scientific computing—contributing to CPython’s datetime, NumPy masked arrays, and Theano’s tensor ops—while also enhancing interactive Python tooling in IPython and Jupyter. His work spans low-level C performance optimizations, WebAssembly runtimes, and front-end UX improvements like notebook keyboard shortcuts and lexers, showing rare full-stack fluency. A master’s-trained applied mathematician, he pairs rigorous numerical reasoning with practical engineering to fix tricky bugs and modernize APIs. Notably, his contributions touch widely used projects in the Python ecosystem, improving both correctness and developer experience for millions of users.
20 years of coding experience
2 years of employment as a software developer
Master's degree, Applied Mathematics, Master's degree, Applied Mathematics at Kharkov State University
Stackoverflow
2,248reputation
101kreached
66answers
3questions
Github Skills (56)
unit-testing10
notebook10
datetime10
zone10
algorithms10
javascript10
pytest10
lexer10
operation10
python10
jupyter10
tensorrt10
bash10
ipython10
c1110
Programming languages (11)
Github contributions (5)
numpy/numpy
Jan 2006 - Aug 2020
The fundamental package for scientific computing with Python.
Role in this project:
Back-end Developer / Software Engineer
Back-end Developer / Software EngineerContributions:91 commits, 5 PRs, 93 comments in 14 years 9 months
Contributions summary:Alex primarily contributed to bug fixes and improvements within the core components of the NumPy library, specifically focused on the masked array functionality. Their work involved fixing bugs related to the handling of masked objects, printing of masked arrays, and implementing new functionalities like `append`, and fixing issues related to the `dot` and `where` operations. The user also implemented and tested various methods for masked arrays, demonstrating a deep understanding of the library's internal workings and its API. They also performed code cleanups and modernized ufunc attribute access.
pythonscientific-computingnumpy
Theano/Theano
Jan 2013 - Jun 2013
Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
Role in this project:Back-end Developer
Back-end DeveloperContributions:102 commits in 4 months
Contributions summary:Alex primarily contributed to the Theano library by implementing and optimizing low-level C code for advanced tensor operations, specifically for `AdvancedSubtensor1` and GPU-related functions. They also addressed several issues within the test suite, including ensuring the proper functioning of the `take` method with and without the use of the `sum` method. Additionally, they merged changes from other branches and fixed a typo. The user's work focused on improving the performance and correctness of core tensor operations within Theano.
mathematical-expressionsmultidimensional-arrayspymcpythontheano
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