Shintaro Shiba
Group Leader, Edge AI HW SW Co-design at The University of Tokyo
Greater Tokyo Area Japan
Summary
👤
Senior🎓
Top SchoolShintaro Shiba is a Group Leader and Project Lecturer specializing in HW/SW co-design for event-driven Edge AI, combining a decade of industry and academic experience across University of Tokyo, Keio, and Woven by Toyota. He holds a PhD jointly from Keio University and Technische Universität Berlin and has led R&D teams that delivered real-world Vision AI products for automotive applications. His research contributions in event-based vision span optical flow, depth, denoising, and computational imaging, earning recognition within the event-camera community and awards such as the Ikushi Prize. Equally comfortable in hardware-aware algorithm design and practical ML deployment, he recruits students and postdocs for projects in event-based computer vision, processor co-design, and robotics. Beyond research, he has improved usability of popular ML tooling through documentation contributions to Chainer, and outside of work he plays jazz saxophone and dances.
10 years of coding experience
8 years of employment as a software developer
University of TokyoDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Technische Universität BerlinDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Keio University
Japanese, English, French, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
python10
cupy10
documentation10
numpy10
chainer10
neural-network9
machine-learning9
deeplearning-ai9
deep-learning9
Programming languages (9)
Github contributions (5)
chainer/chainer
Jan 2017 - Feb 2017
A flexible framework of neural networks for deep learning
Role in this project:
Technical Writer & Documentation Specialist
Technical Writer & Documentation SpecialistContributions:23 commits, 2 PRs, 13 comments in 21 days
Contributions summary:Shintaro primarily focused on enhancing the documentation for the Chainer library. Their contributions involved adding examples, correcting typos, and improving the clarity and formatting of function documentation, particularly for the `linear`, `sigmoid`, `relu`, `clipped_relu`, `crelu`, `elu`, `tanh`, `broadcast`, `broadcast_to`, and `concat` functions. They also updated the documentation to adhere to PEP8 style guidelines and to improve the shape notations. These changes collectively improved the usability and understanding of the Chainer library.
cudapythonmxnetcaffe2flexible-framework
shiba24/deep-learning-for-human-part-discovery-in-images
Aug 2016 - Mar 2017
Contributions:29 commits, 4 PRs, 21 pushes in 6 months
blockchainchainer
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