Shuhei Iitsuka is a Senior UX Engineer and Creative Technologist at Google with 11 years of experience bridging web technology and machine learning to craft polished, data-driven user experiences. He holds a Ph.D. in Engineering from the University of Tokyo, where he researched website optimization with ML, and has published experimental methods for real-world A/B testing. At Google he ships end-to-end solutions—building web frontends, maintaining ML models (e.g., contributions to mozc-devices and Emoji Scavenger Hunt), and improving developer UX through tooling and test automation. His background spans product management and entrepreneurship, giving him a keen sense for turning research insights into production-ready features. Colleagues note his knack for quietly improving code quality and usability through small but impactful changes like style enforcement and CLI/UX refinements.
Budou is an automatic organizer tool for beautiful line breaking in CJK (Chinese, Japanese, and Korean).
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:19 releases, 224 commits, 140 PRs in 6 years 3 months
Contributions summary:Shuhei primarily contributed to the development of unit tests for the `google/budou` repository, a CJK line break organizer. Their work involved writing test cases to validate the core functionalities of the library, including the processing and migration of HTML, demonstrating a focus on ensuring the reliability and correctness of the project's core logic. These unit tests cover a variety of functions, from pre-processing to spanization.
Contributions:20 releases, 588 reviews, 117 commits in 1 year 5 months
Contributions summary:Shuhei contributed to the code quality and style consistency of the project. They added a style check for Python code using Yapf, improving code readability. They also addressed the TypeScript code style using gts, and ensured all code aligned with the project's style guide. Additionally, the user made changes to both Python and JavaScript code, adding and fixing functionality and improving the project's robustness.
nlpjavascriptpythonmachine-learning
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