Nikolay Bushkov
Architect at Aisystant
Moscow, Moscow, Russia
Summary
🤩
Rockstar🎓
Top SchoolNikolay Bushkov is an R&D manager and NLP leader with eight years of cross-disciplinary experience spanning scientific research, bioinformatics, machine learning and production software engineering. He has led NLP teams in banking and enterprise settings, driven principal-level engineering at S&P Global, and translated academic expertise from MIPT into product-focused development. A hands-on contributor to notable open-source ML projects (including test automation for DeepPavlov and batch active learning improvements in modAL), he blends rigorous test-driven practices with algorithmic enhancements to push models safely into production. Known for organizing communication across diverse teams, he excels at turning research and prototypes into robust, deployable products while keeping an eye on practical performance and parallelization challenges.
9 years of coding experience
8 years of employment as a software developer
Master of Science - MS Biotechnology, Master of Science - MS Biotechnology at Moscow Institute of Physics and Technology (State University) (MIPT)
English, Russian
Stackoverflow
41reputation
785reached
1answer
0questions
Github Skills (17)
pytest10
python10
scikit10
machine-learning10
scikit-learn10
active-learning10
test-automation10
machine-learning-algorithms9
sparse-matrix9
ai9
deep-learning9
nlp8
sparse8
natural-language-processing8
tensorflow8
Programming languages (3)
Github contributions (5)
deeppavlov/DeepPavlov
Feb 2018 - Mar 2019
An open source library for deep learning end-to-end dialog systems and chatbots.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:51 commits, 41 PRs, 110 pushes in 1 year 1 month
Contributions summary:Nikolay primarily contributed to the testing framework and test suite of the project. They added initial automatic tests and expanded the test coverage by incorporating tests for custom queries and model interactions. Furthermore, the user refactored and modified the testing procedure, including changes to the download process and configurations for test execution. Their work ensured the reliability and functionality of the DeepPavlov library's models.
deep-learningbotnlpchatbotdialogue-systems
modAL-python/modAL
Sep 2018 - Oct 2018
A modular active learning framework for Python
Role in this project:ML Engineer
ML EngineerContributions:14 commits, 1 PR, 13 comments in 21 days
Contributions summary:Nikolay focused on implementing and improving batch-mode active learning functionality within the `modal-python/modal` repository, which provides active learning framework. Contributions include adding support for sparse matrix handling, integrating the `n_jobs` parameter for parallel processing, and correcting indexing issues within the batch sampling module. These changes enhance the framework's ability to handle diverse data types and improve performance when querying for instance labels.
pythonscikit-learnmachine-learningactive-learningmachine-learning-library
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