Adrian Boguszewski
AI Software Evangelist & Engineer at Intel Corporation
Gdansk, Pomeranian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolAdrian Boguszewski is an AI expert and engineer with 13 years in software and nearly eight years focused on generative AI, deep learning, and computer vision, currently evangelizing AI at Intel. He authored the widely used LandCover.ai dataset, published three research papers, and has spoken at over 40 events including CVPR and PyData, blending academic rigor with developer outreach. Adrian’s open-source contributions span practical model deployment and optimization—adding pose-estimation demos and OpenVINO integration to prominent repos like openvino_notebooks, anomalib, and Ultralytics YOLO. He has led Edge AI reference projects, driven OpenVINO export, INT8 quantization and NNCF compression work, and mentored teams building production-grade detection and segmentation solutions for aerial imagery. Based in Gdańsk with a Master’s in Computer Science, he mixes hands-on ML engineering with developer advocacy, making complex edge-AI tooling accessible to practitioners.
12 years of coding experience
8 years of employment as a software developer
Master’s Degree Computer Science, Master’s Degree Computer Science at Gdańsk University of Technology
English, Polish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (16)
quantization10
computer-vision10
pytorch10
machine-learning10
deeplearning-ai10
jupyter-notebook10
data-export10
exports10
inference10
deep-learning10
onnx10
anomaly-detection10
python10
openvino10
exporter10
Programming languages (6)
Github contributions (5)
openvinotoolkit/openvino_notebooks
Oct 2021 - Jan 2023
📚 Jupyter notebook tutorials for OpenVINO™
Role in this project:
ML Engineer & Backend Developer
ML Engineer & Backend DeveloperContributions:408 reviews, 40 commits, 230 PRs in 1 year 3 months
Contributions summary:Adrian significantly contributed to the implementation of a pose estimation live demo within the OpenVINO notebooks repository. They added the demo, examples for video usage, and incorporated FPS calculations. The user also fixed deprecation warnings and integrated improvements based on code reviews, indicating a focus on performance and code quality. The commits suggest a strong familiarity with computer vision model integration, and the associated code differences reveal the integration of pose estimation using an OpenVINO implementation.
deep-learningjupyter-notebooknotebookinferencecomputer-vision
open-edge-platform/anomalib
Jul 2023 - Mar 2025
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Role in this project:ML Engineer
ML EngineerContributions:23 reviews, 14 PRs, 38 comments in 1 year 8 months
Contributions summary:Adrian's contributions primarily involve fixing and improving the existing notebooks within the repository. These fixes relate to model training and inference, including a notebook demonstrating the use of the Padim model. Further contributions include testing OpenVINO export functionality, fixing PyTorch inference and DSR training, and allowing static input shapes for ONNX and OpenVINO exports. The user has also added NNCF compression/quantization features and added accuracy control quantization.
hyper-parameter-optimizationstate-of-the-arthyperdetection-libraryexperiment-management
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