Reiichiro Nakano
Member Of Technical Staff at Periodic Labs
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolReiichiro Nakano is a software engineer with 10 years of experience in ML-enabled front-end and full-stack development, currently a Member of Technical Staff in the San Francisco Bay Area. He spent five years at OpenAI and has hands-on experience shipping production JavaScript ML (TensorFlow.js) features—implementing core math ops, GPU/CPU kernels, and rich browser UIs for in-browser style transfer with webcam support. Reiichiro combines a foundation in electronics engineering (M.Sc.) with practical data-science tooling contributions (mlxtend, scikit-plot), demonstrating fluency across model development, evaluation, and visualization. He gravitates toward projects that bridge research and UX, improving developer-facing libraries while making ML accessible in the browser. An active open-source maintainer, he pairs careful testing and API work with polish on user interfaces, a blend that helped shape tfjs-core and several popular demo repos.
10 years of coding experience
8 years of employment as a software developer
M.Sc Electronics Engineering, M.Sc Electronics Engineering at De La Salle University
English, Tagalog, Japanese
Github Skills (38)
webgl10
javascript10
python10
data-science10
scikit10
gpu-programming10
tensorflowjs10
machine-learning10
typescript10
ui-design10
plot10
style-transfer10
deep-learning10
front-end-development10
supervised-learning10
Programming languages (16)
Github contributions (5)
reiinakano/scikit-plot
Feb 2017 - Aug 2018
An intuitive library to add plotting functionality to scikit-learn objects.
Role in this project:
Data Scientist
Data ScientistContributions:17 releases, 116 commits, 80 PRs in 1 year 6 months
Contributions summary:Reiichiro implemented several functions to enhance scikit-plot's functionality. This included developing a function and test to plot the learning curve. The user also added the plot_confusion_matrix function. Furthermore, the user developed the plot_roc_curve, plot_ks_statistic, plot_precision_recall_curve, and plot_feature_importances functions and their associated examples and tests. Lastly, the user improved code style, documentation, and example images.
pythondata-sciencemachine-learningintuitivescikit-learn
reiinakano/arbitrary-image-stylization-tfjs
Nov 2018 - Apr 2019
Arbitrary style transfer using TensorFlow.js
Role in this project:Front-end Developer & ML Engineer
Front-end Developer & ML EngineerContributions:64 commits, 29 PRs, 91 pushes in 5 months
Contributions summary:Reiichiro primarily worked on implementing the front-end interface for an image style transfer application using TensorFlow.js. They added features such as image selection, size adjustments, and a slider for controlling stylization strength. Furthermore, the user integrated the loading and use of both a MobileNet and an InceptionV3 style model, as well as the separable transformer model, indicating a strong understanding of the underlying machine learning techniques. The contributions also included adding an option for combining styles, webcam support and improved the overall user experience through interface enhancements.
style-transferbrowsergenerative-artdeep-learningjavascript
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