Kobayashi Sosuke

Specially Appointed Associate Professor (Visiting)

Tokyo, Japan
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts

Summary

🤩
Rockstar
🎓
Top School
Kobayashi Sosuke is a researcher and engineer with a decade of experience specializing in ML, NLP, and computer vision, currently balancing roles as a Researcher at Preferred Networks and a Specially Appointed Associate Professor (Visiting) at Tohoku University. He holds a PhD in System Information Sciences from Tohoku University and progressed through its bachelor's and master's programs, reflecting deep academic roots tied to practical industry research. His open-source contributions include implementing and testing TreeLSTM units in the widely used Chainer deep learning framework, showcasing hands-on expertise in neural network internals and model engineering. Comfortable bridging academic research and product-focused development, he brings rigorous evaluation practices and code-quality attention to applied ML projects in Tokyo.
code10 years of coding experience
job3 years of employment as a software developer
bookDoctor's Degree, System Information Sciences, Doctor's Degree, System Information Sciences at 東北大学
github-logo-circle

Github Skills (9)

neural-network10
machine-learning10
deep-learning10
python10
chainer10
numpy9
unit-testing8
gpu7
cuda7

Programming languages (3)

CSSJupyter NotebookPython

Github contributions (5)

github-logo-circle
chainer/chainer

Feb 2017 - Feb 2018

A flexible framework of neural networks for deep learning
Role in this project:
userBack-end Developer & ML Engineer
Contributions:59 commits, 14 PRs, 44 pushes in 11 months
Contributions summary:Kobayashi primarily contributed to the Chainer deep learning framework, focusing on the implementation and testing of TreeLSTM (Tree Long Short-Term Memory) units within the library. Their work involved modifying existing code related to ResNet models and making adjustments to the TreeLSTM module for improved functionality. They also added tests to validate the new TreeLSTM implementation and addressed code style issues.
cudapythonmxnetcaffe2flexible-framework
Contributions:26 commits, 23 pushes, 1 branch in 3 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial