Hideaki Imamura
リサーチャー at Preferred Networks, Inc.
Tokyo, Japan
Summary
🤩
Rockstar🎓
Top SchoolHideaki Imamura is a researcher at Preferred Networks with eight years’ experience focused on machine learning and AutoML, where he contributes to both core research and production tooling. Based in Tokyo, he is a key developer and documentation specialist for Optuna, the widely used hyperparameter optimization framework, improving its suggest API, back-end stability, and user-facing docs. With a master's in Computer Science from the University of Tokyo, he combines academic rigor with pragmatic engineering to bridge research and usable software. Colleagues rely on him for clean API refactors, bug fixes in core sampling logic, and clear documentation that reduces onboarding friction for ML practitioners.
8 years of coding experience
修士（理工学）, Computer Science, 修士（理工学）, Computer Science at 東京大学
Github Skills (9)
parameter-tuning10
python10
optimisation10
documentation10
optimization10
apidoc9
api9
ml7
machine-learning7
Programming languages (5)
Github contributions (5)
optuna/optuna
Nov 2018 - Jan 2023
A hyperparameter optimization framework
Role in this project:
Back-end Developer & Documentation Specialist
Back-end Developer & Documentation SpecialistContributions:15 releases, 1955 reviews, 1297 commits in 4 years 2 months
Contributions summary:Hideaki appears to have worked on the documentation and core functionality of the Optuna library. Their commits include merging master, refactoring suggest API and other API improvements, and fixing various bug fixes related to the parameter suggest functions. Additionally, the user's commits include enhancements to the documentation, addressing grammar errors, and improving clarity, specifically in the FAQ section, which suggests a focus on back-end development and documentation.
pythonoptimization-frameworkparallelhyperparameteroptimization
optuna/optuna-examples
May 2021 - Mar 2025
Examples for https://github.com/optuna/optuna
Contributions:29 reviews, 40 PRs, 26 pushes in 3 years 10 months
pythonparallelmachine-learningoptunahyperparameter-optimization
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