Yuta Yamazaki
Senior Engineer 主任技師 at 日立産業制御ソリューションズ
Fukushima, Japan
Summary
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Senior🎓
Top SchoolYuta Yamazaki is a senior automotive software engineer and team lead based in Fukushima with over a decade of hands-on experience in embedded vehicle software, AUTOSAR modules, and program-level delivery. He progressed from requirement analysis and design to team lead, project manager, and program manager roles at Hitachi, and currently serves as Senior Engineer / 主任技師 at 日立産業制御ソリューションズ. Certified as an Automotive SPICE provisional assessor and a PMP, he blends process rigor with pragmatic engineering to deliver safety-critical automotive software. He also contributes to open-source ML projects—improving Optuna examples and integrating NGBoost with scikit-learn—illustrating a rare cross-domain fluency between embedded systems and machine learning tooling. Known for moving between hands-on coding and project leadership, he often bridges supplier AUTOSAR modules and in-house development teams to accelerate integration and quality.
8 years of coding experience
14 years of employment as a software developer
学士, electrical & informtion, 学士, electrical & informtion at 釧路工業高等専門学校
Github Skills (15)
scikit10
hyperparameter-optimization10
machine-learning10
api10
python10
apidoc10
optuna10
gradient-boosting10
scikit-learn10
model-testing9
estimate9
evaluation9
eval9
uncertainty9
documentation8
Programming languages (8)
Github contributions (5)
optuna/optuna
Nov 2019 - Feb 2020
A hyperparameter optimization framework
Role in this project:
ML Engineer
ML EngineerContributions:36 commits, 5 PRs, 15 comments in 2 months
Contributions summary:Yuta contributed to examples showcasing the use of `optuna.integration.OptunaSearchCV` for hyperparameter optimization of a classifier on the Iris dataset. They modified the example by adding additional hyperparameter options and improved the description. The changes focused on the integration of Optuna with scikit-learn's `SVC` and demonstrate how to tune model parameters using Optuna's search capabilities.
pythonoptimization-frameworkparallelhyperparameteroptimization
stanfordmlgroup/ngboost
Nov 2019 - Mar 2020
Natural Gradient Boosting for Probabilistic Prediction
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 9 PRs, 5 comments in 4 months
Contributions summary:Yuta contributed to the development and integration of the scikit-learn API for the NGBoost library. They implemented example code for using NGBoost with scikit-learn for both regression and classification tasks, updated documentation and examples, and fixed related import issues. Further contributions involved cleaning the codebase and optimizing the implementation of the model.
pythonpredictionnaive-bayesnatural-gradientsmachine-learning
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