Keisuke Fukuda
Engineering Manager at Tokyo Institute of Technology
Chiyoda, Japan
Summary
🤩
Rockstar🎓
Top SchoolKeisuke Fukuda is an engineering manager at Preferred Networks with 11 years of experience specializing in high-performance computing, GPGPU programming, scientific simulation, and deep learning. He combines hands-on ML engineering—contributing example implementations and optimizations for flagship projects like Chainer—with QA/test automation work in tooling such as Optuna, showing a rare blend of model-centric and infrastructure-focused expertise. Based in Chiyoda, Japan, he bridges research and production as a long-running contributor and Ph.D. candidate at Tokyo Institute of Technology, applying rigorous academic methods to real-world systems. Known for optimizing image-classification pipelines (ResNet50, AlexNet) and improving visualization test coverage, he brings both low-level performance tuning and high-level validation discipline to teams. Colleagues value him for turning complex numerical problems into maintainable, high-throughput code and for mentoring engineers on reproducible ML workflows.
11 years of coding experience
BS, Information Science, BS, Information Science at 東京工業大学Master of Science (M.S.), Mathematical and Computing Sciences, Master of Science (M.S.), Mathematical and Computing Sciences at 東京工業大学 / Tokyo Institute of Technology
Stackoverflow
86reputation
2kreached
2answers
2questions
Github Skills (18)
visualization10
pytest10
python10
imagenet10
chainer10
testing10
machine-learning10
deep-learning10
resnet10
neural-network10
visualizations10
gpu9
hyperparameter-optimization9
cuda8
cudnn8
Programming languages (10)
Github contributions (5)
chainer/chainer
Apr 2017 - Jul 2020
A flexible framework of neural networks for deep learning
Role in this project:
ML Engineer
ML EngineerContributions:163 commits, 65 PRs, 51 pushes in 3 years 3 months
Contributions summary:Keisuke primarily contributed to the `examples/imagenet` directory, adding and modifying example implementations of neural networks for image classification, specifically ResNet50 and AlexNet. Their work involved debugging, refactoring, and optimizing these example models within the Chainer framework. The user also addressed minor issues such as fixing flake8 errors and removing unused variables.
cudapythonmxnetcaffe2flexible-framework
optuna/optuna
Dec 2021 - Feb 2022
A hyperparameter optimization framework
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:6 reviews, 9 commits, 3 PRs in 1 month
Contributions summary:Keisuke primarily contributed to the testing of the Optuna framework, focusing on visualization features. They added and modified tests within the `test_slice.py` file, ensuring correct functionality and data representation in plots generated by the framework. These contributions involved creating tests to validate plot outputs, including data correctness and label accuracy. In addition, the user addressed minor code style issues.
pythonoptimization-frameworkparallelhyperparameteroptimization
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