Shinya Suzuki
Staff Engineer at Asahi Kasei
Chiyoda, Japan
Summary
👤
Senior🎓
Top SchoolShinya Suzuki is a Staff Engineer and Ph.D.-trained research engineer based in Chiyoda, Japan, with 10 years of experience applying numerical analysis, machine learning, and scientific methods to real-world problems. He excels at cross-disciplinary collaboration, designing and refining experiments, and building robust software that drives measurable impact across domains like biostatistics, bioinformatics, chemoinformatics, and materials informatics. A practical open-source contributor, he added the Brunner–Munzel test to SciPy’s stats module—complete with tests, NaN handling, and documentation—illustrating his attention to statistical rigor and production-quality code. At Asahi Kasei he blends research depth with engineering delivery, translating generative-AI and Bayesian techniques into usable systems. His background (BEng, MEng, PhD from Tokyo Institute of Technology) underpins a rare combination of theoretical breadth and hands-on implementation.
10 years of coding experience
Doctor of Science - Ph.D., Doctor of Science - Ph.D. at Tokyo Institute of Technology
Japanese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (12)
statistics10
algorithms10
scipy10
python10
scientific-computing10
statistic10
testing10
pytest9
numpy9
unit-test9
unit-testing9
documentation7
Programming languages (15)
Github contributions (5)
scipy/scipy
Apr 2017 - Nov 2017
SciPy library main repository
Role in this project:
Data Scientist
Data ScientistContributions:8 commits, 1 PR, 1 comment in 6 months
Contributions summary:Shinya primarily contributed to the implementation and testing of the Brunner-Munzel test within the SciPy library. Their work involved adding the test function, integrating it into the `stats` module, writing comprehensive test cases to validate the functionality, and fixing bugs identified during the review process. Further contributions included documentation updates and enhancements to handle NaN inputs correctly. This work significantly expanded the statistical capabilities of the SciPy library.
scipypythonscientific-computing
TaskeHAMANO/SPHERE
Sep 2017 - Nov 2019
Synthetic PHasE Rate Estimator by single metagenome sequence
Contributions:441 commits, 2 PRs, 10 pushes in 2 years 1 month
sequencemetagenomebioinformaticssyntheticphase
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