Kei Ishikawa
Japan
Summary
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RockstarKei Ishikawa is an engineer with 8 years of multidisciplinary experience bridging thermal research and software development, focused on heat transfer and thermal property measurement of single-walled carbon nanotubes. He progressed through bachelor’s, master’s, doctoral and postdoctoral work in heat transfer, where he launched a new research direction for his group from scratch during his Ph.D. Beyond academia, Kei contributes to open-source ML tooling—most notably improving the TPE sampler and multivariate Parzen estimator in the widely-used Optuna optimization framework. He combines low-level skills in C and UNIX with analog circuit design and thermal experiment expertise, making him adept at turning physical measurement challenges into robust, reproducible code and instrumentation. Based in Japan, he brings a rare blend of hands-on lab practice and backend/ML engineering to projects that sit at the intersection of hardware and software.
8 years of coding experience
Github Skills (8)
algorithm10
data-structures10
hyperparameter-optimization10
algorithms10
machine-learning10
python10
data-structure10
numpy9
Programming languages (6)
Github contributions (5)
optuna/optuna
Aug 2020 - Jan 2022
A hyperparameter optimization framework
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:140 reviews, 70 commits, 9 PRs in 1 year 5 months
Contributions summary:Kei primarily contributed to the `optuna/optuna` repository by updating the TPE sampler and related multivariate parzen estimator. The user's commits modified and added code related to the core algorithms and parameters of the TPE sampler. They also addressed issues and improved the code regarding categorical distributions.
pythonoptimization-frameworkparallelhyperparameteroptimization
kstoneriv3/Fake-Voice-Detection
Dec 2018 - Nov 2019
For "Deep Learning class" at ETHZ. Evaluate how well the fake voice of Barack Obama 1. confuses the voice verification system, 2. can be detected. The report of this project is available at ->
Contributions:24 commits, 23 pushes, 1 branch in 10 months
verification-systemevaluatedeep-learningreportvoice
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