Takeshi Oura
Software Engineer at SB Intuitions
Chiyoda, Japan
Summary
👤
Senior🎓
Top SchoolTakeshi Oura is a software engineer with 10 years of experience blending data science, machine learning, and backend engineering to build scalable, production-grade systems. He has applied deep learning and ML to product search and recommendation at MonotaRO and led server-side engineering for large-scale game features at Niantic, now bringing that cross-disciplinary rigor to SB Intuitions. With a Ph.D. in statistical physics from Osaka University, he combines strong quantitative modeling skills with practical software craftsmanship. He contributes to open-source testing for Optuna, improving robustness around visualization and edge-case handling, reflecting a focus on reliability often overlooked in ML tooling. Based in Chiyoda, Japan, he thrives at the intersection of research-grade methods and production engineering to improve user experience and strategic decision-making.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Statistical Physics, Doctor of Philosophy (Ph.D.), Statistical Physics at Osaka University理学修士, 物理学, 理学修士, 物理学 at 大阪大学
Stackoverflow
363reputation
33kreached
11answers
0questions
Github Skills (15)
pytest10
python10
testing10
visualizations9
machine-learning9
visualization9
hyperparameter-optimization9
pandas6
matrix6
loops6
forloop6
numpy6
matplotlib6
sorting6
mergesort6
Programming languages (1)
Github contributions (5)
optuna/optuna
Apr 2022 - May 2022
A hyperparameter optimization framework
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:2 reviews, 6 commits, 2 PRs in 7 days
Contributions summary:Takeshi's contributions center around improving testing procedures within the Optuna framework. They primarily focused on adding and updating unit tests, specifically targeting visualization components and parameter importance calculations. The user's work aimed to ensure the robustness of the library by adding tests that handle edge cases, such as infinite objective values, and by ensuring that existing tests are up-to-date. These changes contribute to the overall reliability of the hyperparameter optimization framework.
pythonoptimization-frameworkparallelhyperparameteroptimization
takoika/scikit-learn
May 2021 - Jan 2022
scikit-learn: machine learning in Python
Contributions:2 PRs, 67 pushes, 8 branches in 8 months
pythondata-sciencelearn-machine-learningmachine-learningscikit-learn
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