Kento Nozawa
Engineer at Preferred Networks, Inc.
Tokyo, Japan
Summary
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Rockstar🎓
Top SchoolKento Nozawa is an engineer and machine learning specialist based in Tokyo with 11 years of experience spanning research and industry roles at Preferred Networks, IBM, RIKEN AIP, and AIST. He holds advanced academic training toward a PhD in Computer Science from the University of Tokyo and has a strong foundation in information and library science from the University of Tsukuba. Kento contributes to prominent open-source ML projects—improving PyTorch text processing, Keras documentation, and Optuna’s sampling capabilities—demonstrating a focus on code quality, usability, and robust testing. His background blends hands‑on backend and ML engineering with research rigor, having moved between internships, research assistantships, and production ML roles. Colleagues would note his quieter attention to detail: many of his contributions are small but impactful fixes that improve developer experience and reliability. He prefers to focus on technical collaboration and politely filters outreach about marketing, webinars, or irrelevant roles.
11 years of coding experience
8 years of employment as a software developer
University of TokyoInformation Science, Information Science at University of Tsukuba
Japanese
Stackoverflow
378reputation
15kreached
14answers
0questions
Github Skills (19)
pytorch10
python10
testing10
machine-learning10
hyperparameter-optimization10
keras10
data-loading10
deep-learning10
nlp10
documentation10
tensorflow9
neural-network9
modeling8
datasets8
pruning6
Programming languages (14)
Github contributions (5)
optuna/optuna
Apr 2020 - Jan 2023
A hyperparameter optimization framework
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1042 reviews, 769 commits, 404 PRs in 2 years 9 months
Contributions summary:Kento primarily contributed to the implementation of core features within the Optuna framework, with a focus on enhancing its machine learning capabilities. The user's work included adding the functionality to suggest float parameters, including discrete versions, and enabling the use of steps within the Suggest function as well as enabling log option for suggesting integer values. The user also made improvements and modifications to existing distributions and samplers for more advanced functionality. The user's contributions also involve tests related to these features.
pythonoptimization-frameworkparallelhyperparameteroptimization
keras-team/keras
May 2016 - Oct 2017
Deep Learning for humans
Role in this project:ML Engineer & Technical Writer
ML Engineer & Technical WriterContributions:35 commits, 37 PRs, 39 comments in 1 year 5 months
Contributions summary:Kento primarily contributed to improving the Keras documentation and addressing minor issues related to the codebase. Their work included fixing typos, updating docstrings, and refining documentation for various layers and methods, such as `Bidirectional`, `SpatialDropouts`, and `Sequence`. They also made minor code adjustments like removing unused import statements and updating default dimension ordering. These changes reflect a focus on code clarity and documentation accuracy.
pythondata-sciencedeep-learningneural-networksmachine-learning
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