Shigeki Karita
Research Software Engineer at Google
Kyoto, Japan
Summary
🤩
Rockstar🎓
Top SchoolShigeki Karita is a Research Software Engineer based in Kyoto with 11 years of experience bridging research and production-grade software, currently at Google after four years as a researcher at NTT. He specializes in backend development, QA and test automation, with notable open-source contributions to high-profile projects like the D compilers (dmd, ldc) and Chainer, where he focused on correctness around exception handling, CUDA kernel optimizations, and robust testing. His work on latexify_py shows an attention to code quality and tooling, adding tests and linting fixes to improve maintainability. Trained as an electrical and electronics engineer (MEng, Osaka University), he combines rigorous academic grounding with practical systems-level expertise in compilers, deep learning frameworks, and developer tooling. An understated strength is his knack for small, precise tests and refactors that fix subtle language/runtime bugs and prevent regressions in complex codebases.
11 years of coding experience
4 years of employment as a software developer
Master of Engineering - MEng, Electrical and Electronics Engineering, Master of Engineering - MEng, Electrical and Electronics Engineering at Osaka University
Github Skills (27)
unit-testing10
python10
testing10
dword10
numpy10
kernel10
latex10
compiler-compiler10
dmd10
gpu10
cupy10
dt10
cuda10
compiler10
programming-language9
Programming languages (12)
Github contributions (5)
google/latexify_py
Sep 2020 - Sep 2020
A library to generate LaTeX expression from Python code.
Role in this project:
Back-end Developer & QA Engineer
Back-end Developer & QA EngineerContributions:53 reviews, 8 commits, 2 PRs in 1 day
Contributions summary:Shigeki primarily contributed to the `latexify_py` library by addressing both code improvements and testing. They fixed issues in the `test_io.py` and `core.py` files, replacing instances of `\operatorname` with `\mathrm`. Further, they added pylint and addressed reported errors and warnings, indicating a focus on code quality and maintainability. The user also added tests for the library's core functionality.
python-codepythonlatex
chainer/chainer
Dec 2016 - Mar 2017
A flexible framework of neural networks for deep learning
Role in this project:Back-end Developer
Back-end DeveloperContributions:15 commits, 3 PRs, 12 comments in 3 months
Contributions summary:Shigeki primarily focused on optimizing and refactoring CUDA kernel implementations within the `chainer/chainer` repository. Their work included fixing and enhancing optimizer hooks by integrating CUDA kernels. The user also refactored and reformatted code, addressing linter issues and improving code structure related to sum and product computations. They added and modified tests and made minor edits to the repository's CUDA-related files.
cudapythonmxnetcaffe2flexible-framework
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