Shintaro Okada
Senior Researcher at Samsung R&D institute Japan
Chiyoda, Japan
Summary
🤩
Rockstar🎓
Top SchoolShintaro Okada is a Senior Researcher with over two decades of hands-on experience in image processing, specializing in high-picture-quality algorithms for TV and display SoCs and surveillance camera platforms. After a 15-year tenure at Sony, he has led advanced imaging R&D at Samsung R&D Institute Japan since 2013, bridging product-grade engineering with research-driven innovation. More recently he has integrated machine learning into image analysis pipelines, contributing practical models such as MobileNetV2 to the ChainerCV ecosystem. A pragmatic C++ developer, he also maintains clean, well-tested libraries like a header-only SHA256 implementation and has improved visualization and multi-objective handling in Optuna. Based in Chiyoda, Tokyo, he combines deep academic grounding from Chuo University with a steady track record of shipping robust, production-focused imaging solutions. Notably, his work spans both low-level optimized implementations and ML model integration, making him effective across the full imaging stack.
13 years of coding experience
15 years of employment as a software developer
Master, Division of Science and Engineering, Master, Division of Science and Engineering at Chuo University Graduate School
Github Skills (25)
algorithm10
cpp-library10
algorithms10
visualization10
c-language10
python10
sha10
chainer10
machine-learning10
data-structure10
hyperparameter-optimization10
headeronly10
deep-learning10
tensorflow10
mobilenet10
Programming languages (5)
Github contributions (5)
okdshin/PicoSHA2
Feb 2014 - Aug 2022
a header-file-only, SHA256 hash generator in C++
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 4 reviews, 80 commits in 8 years 7 months
Contributions summary:Shintaro primarily focused on implementing a header-file-only SHA256 hash generator in C++. Their commits demonstrate a deep understanding of cryptographic principles, evident in their implementation of core functions such as `hash_block` and `hash256`. They also added helper functions and unit tests, improving the library's usability and correctness. The user also provided examples for testing, suggesting a focus on creating a well-documented and functional library.
mit-licensecpphashheadersha256
chainer/chainercv
Mar 2019 - Dec 2019
ChainerCV: a Library for Deep Learning in Computer Vision
Role in this project:ML Engineer
ML EngineerContributions:36 commits, 3 PRs, 19 comments in 9 months
Contributions summary:Shintaro primarily contributed to the implementation of MobileNetV2 within the ChainerCV library. Their work included adding the MobileNetV2 model, incorporating functionality for different categories, and converting weights from a TensorFlow checkpoint. They also modified the feature predictor to handle scaling, improving the model's usability. Furthermore, the user added tests and example files, demonstrating an understanding of model integration and evaluation.
deep-learningpytorchcomputer-visionvision
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