Ryuichiro Hataya
Program-Specific Assistant Professor at SB Intuitions
Chiyoda, Japan
Summary
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Rockstar🎓
Top SchoolRyuichiro Hataya is a Program-Specific Assistant Professor and Senior Research Scientist with 11 years of experience at the intersection of academic research and applied machine learning in Japan. He holds a PhD in Information Science and Technology from the University of Tokyo and begun blending research and industry roles in 2025, bridging Kyoto University and SB Intuitions. His hands-on ML engineering work includes refactoring and improving a PyTorch SENet implementation with ImageNet and CIFAR-10 support, reflecting a focus on model robustness and training pipelines. Ryuichiro’s background in administration engineering and advanced information science equips him to translate theoretical ideas into reproducible code and performant models. Based in Chiyoda, he combines rigorous academic methodology with practical engineering habits, often surfacing in meticulous bug fixes and preprocessing optimizations. Colleagues describe him as someone who quietly advances projects by tightening implementation details that materially improve model behavior.
11 years of coding experience
Bachelor of Engineering - BE, Administration Engineering, Bachelor of Engineering - BE, Administration Engineering at 慶應義塾大学Master's degree, Master's degree at 東京大学
Github Skills (8)
mask-rcnn10
faster-rcnn10
pytorch10
image-recognition10
machine-learning10
resnet10
imagenet9
cifar1009
Programming languages (9)
Github contributions (5)
moskomule/senet.pytorch
Sep 2017 - Mar 2021
PyTorch implementation of SENet
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 62 commits, 11 PRs in 3 years 6 months
Contributions summary:Ryuichiro primarily refactored and updated the SENet implementation using PyTorch, adding and modifying key components such as the SELayer and various ResNet architectures. They integrated SE-Inception v3 and made adjustments to global maxpooling and bug fixes within the execution and utility files. Furthermore, they added ImageNet and CIFAR10 support with specific data preprocessing and optimization strategies, showcasing a focus on model training and performance enhancements.
pytorchimagenetsenetefficientnetdeep-learning
moskomule/pytorch.rl.learning
Aug 2017 - Oct 2019
for learning reinforcement learning using PyTorch.
Contributions:44 commits, 1 PR, 25 pushes in 2 years 2 months
pytorchmultiagent-reinforcement-learningactor-criticreinforcement-learninggymnasium
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