Shotaro Ishihara
Senior Research Scientist at Nikkei
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolShotaro Ishihara is a Senior Research Scientist based in Tokyo with eight years of experience applying machine learning and data science to news media products at Nikkei. He bridges academic rigor from The University of Tokyo with newsroom experience—having served as an editor and data analyst for the university paper—fueling research into how large language models can reshape journalism. A frequent competitor and mentor in the ML community, he won a Kaggle competition, hosted Kaggle Days Tokyo, and contributes to notable open-source tooling such as Optuna, improving type safety and maintainability. His work earned the INMA “30 Under 30” Grand Prize in 2020 and includes three published books, reflecting a rare combination of research, product impact, and public-facing communication.
8 years of coding experience
5 years of employment as a software developer
University of Tokyo
Chinese
Github Skills (7)
hyperparameter-optimization10
typehinting10
type-checking10
python10
optuna10
machine-learning9
integration-testing8
Programming languages (6)
Github contributions (5)
optuna/optuna
Oct 2019 - Sep 2020
A hyperparameter optimization framework
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 reviews, 26 commits, 6 PRs in 11 months
Contributions summary:Shotaro primarily focused on enhancing type safety and code quality within the Optuna framework. Their contributions included adding type hints to several Python files within the `optuna/integration/_lightgbm_tuner` and `optuna/trial` directories. They also refactored the code to use `Any` instead of `List[Any]` in certain areas, improving code readability and maintainability. Furthermore, they added a metric alias for LightGBMTuner.
pythonoptimization-frameworkparallelhyperparameteroptimization
upura/semeval2022-task8-multilingual-news-article-similarity
Jan 2022 - Jul 2022
Contributions:49 commits, 1 push in 6 months
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