Kenta Oono
Chiyoda, Japan
Summary
🤩
Rockstar🎓
Top SchoolKenta Oono is an engineer and University of Tokyo PhD candidate specializing in theoretical analysis of machine learning and deep learning, with a particular focus on graph neural networks and applications to life sciences. With 11 years of experience across Preferred Infrastructure, Preferred Networks, and freee K.K., he blends research rigor with production engineering, having moved from hands-on backend and GPU-accelerated deep learning work to engineering management. He has contributed to influential open-source projects such as Chainer and CuPy, improving GPU integration, testing infrastructure, and documentation for widely used scientific Python tooling. His CV combines mathematical depth (BSc/MSc from the University of Tokyo) with practical data-analysis projects on biological data, and he frequently teaches and gives invited talks on DL theory and implementation. Less obvious: he pairs theoretical research with careful test- and documentation-first contributions, helping ensure reproducibility and robustness in GPU numerical code.
11 years of coding experience
15 years of employment as a software developer
University of Tokyo
English, Japanese
Github Skills (13)
cuda10
gpu-programming10
machine-learning10
test-framework10
deep-learning10
python10
cupy10
chainer10
numpy10
testing10
neural-network9
documentation8
cudnn8
Programming languages (7)
Github contributions (5)
chainer/chainer
Jun 2015 - Oct 2018
A flexible framework of neural networks for deep learning
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:5 releases, 482 commits, 774 PRs in 3 years 4 months
Contributions summary:Kenta contributed to the Chainer deep learning framework by primarily focusing on improvements and bug fixes related to the CuPy integration for GPU acceleration. Their work included fixing documentation, correcting array handling, updating testing modules, and addressing issues related to specific functionalities like Maxout. Additionally, they demonstrated expertise in maintaining and improving the consistency and correctness of the CuPy and NumPy implementations within the Chainer framework. The contributions suggest a deep understanding of both deep learning concepts and GPU programming for performance optimization.
cudapythonmxnetcaffe2flexible-framework
cupy/cupy
May 2017 - Jun 2018
NumPy & SciPy for GPU
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:3 releases, 11 commits, 22 PRs in 1 year
Contributions summary:Kenta primarily contributed to improving the testing infrastructure and documentation within the CuPy repository. Their work included fixing documentation issues, registering docstrings to the manual, and adding and correcting test cases. They also addressed issues related to specific testing functions like `assert_array_almost_equal_nulp` and incorporated parameterized testing for dtypes.
cudapythoncusolvergpunumpy
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