Soramichi Akiyama
Associate Professor
Japan
Summary
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Rockstar🎓
Top SchoolSoramichi Akiyama is an associate professor at Ritsumeikan University with 11 years of experience bridging academic research and practical systems engineering in information science. He leads the Advanced Systems research group and has held faculty and research positions at The University of Tokyo, AIST, ETH Zürich, and industry labs including Microsoft Research and NTT. His PhD from The University of Tokyo underpins a career that spans research into system architectures and hands-on contributions to open-source deep learning and GPU libraries—most notably test and QA work for Chainer and CuPy that improved reliability across CPU/GPU boundaries. Akiyama combines rigorous academic training with practical test-automation expertise, making him adept at turning experimental ideas into reproducible, production-ready components. An understated strength is his focus on extension handling and type-safe implementations in ML frameworks, reflecting attention to both developer ergonomics and correctness.
11 years of coding experience
1 year of employment as a software developer
University of TokyoBachelor of Engineering (B.Eng.), Computer Science, Bachelor of Engineering (B.Eng.), Computer Science at Kyoto University
Japanese, English
Github Skills (30)
assembly10
c-language10
intel10
python10
architecture10
chainer10
computer-architecture10
testing10
cpu10
cpu-architecture10
machine-learning10
linux10
performance-counters10
numpy10
deeplearning-ai10
Programming languages (9)
Github contributions (5)
chainer/chainer
Nov 2016 - Apr 2017
A flexible framework of neural networks for deep learning
Role in this project:
Backend Developer & Test Automation Engineer
Backend Developer & Test Automation EngineerContributions:44 commits, 18 PRs, 44 comments in 5 months
Contributions summary:Soramichi primarily contributed to the Chainer deep learning framework by fixing documentation errors and adding tests to ensure functionality. They focused on the extension handling within the training module, including testing the integration of lambda functions and callable classes. Furthermore, the user addressed type-related issues, incorporating type checks within the code and modifying the batch normalization function.
cudapythonmxnetcaffe2flexible-framework
cupy/cupy
Jun 2017 - Jun 2017
NumPy & SciPy for GPU
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:1 issue in 1 day
Contributions summary:Soramichi's primary focus appears to be on testing and quality assurance within the CuPy library. Their commits include adding and modifying tests for various functionalities, including extensions, trainer behavior, and mixed CPU/GPU usage. The user's work ensures the reliability and correctness of the library by verifying functionalities and compatibility with various hardware configurations.
cudapythoncusolvergpunumpy
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