Michał Krawczyk
Artificial Intelligence Research Engineer Specialist at Tailored.AI - Michał Krawczyk
Wroclaw Metropolitan Area Poland
Summary
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Rockstar🎓
Top SchoolMichał Krawczyk is an AI research engineer with nine years of experience specializing in deep learning and computer vision, currently running Tailored.AI to deliver bespoke AI solutions and consultancy from model design to production deployment. He has led and deployed vehicle-recognition and license-plate systems on national roads, optimized model architectures for faster inference, and overseen data annotation quality as an AI lead. A regular contributor to scikit-image, he’s improved numerical stability and multichannel processing in a widely used open-source image-processing library, reflecting a strong grounding in low-level algorithmic robustness. Based in Wrocław, he combines practical engineering with ongoing scientific curiosity—publishing, experimenting, and prototyping IoT-integrated AI applications in his spare time.
9 years of coding experience
Engineer, Electronics / Applied Control Electronics, Engineer, Electronics / Applied Control Electronics at Wrocław University of Science and Technology
Github Skills (17)
pytest10
python10
image-processing10
testing10
scikit-image10
numpy10
computer-vision10
conversions9
data-conversion9
convertion9
type-conversion9
optimization8
optimisation8
algorithms8
numerical-optimization8
Programming languages (4)
Github contributions (5)
scikit-image/scikit-image
Mar 2021 - Jun 2021
Image processing in Python
Role in this project:
Software Engineer (Image Processing Focus)
Software Engineer (Image Processing Focus)Contributions:8 reviews, 19 commits, 1 PR in 3 months
Contributions summary:Michał primarily contributed to the improvement and maintenance of the `scikit-image` library. Their work involved refining core functionalities like the Gaussian filter and data type conversions, as evidenced by code changes in `dtype.py` and `_gaussian.py`. They also updated and added tests to ensure the quality and correctness of the implemented features, focusing on areas such as multichannel image processing and the `preserve_range` parameter. These contributions reflect a focus on improving the numerical stability and broader utility of the core image processing algorithms.
image-processingpythoncomputer-visionimage
michalkrawczyk/scikit-learn
Apr 2021 - Apr 2022
scikit-learn: machine learning in Python
Contributions:140 pushes, 2 branches in 1 year
pythondata-sciencelearn-machine-learningneural-networksmachine-learning
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