Keita Kurita
Student
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolKeita Kurita is an ML engineer with a decade of experience, currently focused on building reliable, production-ready ML systems at Robust Intelligence. A dual alumnus of the University of Tokyo and Carnegie Mellon (MCDS, 4.18/4.3), he blends strong academic rigor with practical engineering across NLP and data pipelines. He contributes to core open-source tooling—having improved PyTorch's text data loaders and Field utilities—helping make language data ingestion more robust and flexible. Based in Cambridge, MA, he pairs backend engineering chops with an interest in deployment and model reliability, and he shares technical insights through an irregular but thoughtful blog.
10 years of coding experience
Bachelor's degree, Electrical Engineering and Information Communication, 4.18/4.3, Bachelor's degree, Electrical Engineering and Information Communication, 4.18/4.3 at University of TokyoMasters, Master of Computational Data Science, School of Computer Science, 4.18/4.3, Masters, Master of Computational Data Science, School of Computer Science, 4.18/4.3 at Carnegie Mellon University
Japanese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
data-loading10
pytorch10
nlp10
python10
datasets10
testing9
data-structures8
data-structure8
Programming languages (4)
Github contributions (5)
pytorch/text
Feb 2018 - Sep 2018
Models, data loaders and abstractions for language processing, powered by PyTorch
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 commits, 12 PRs, 13 comments in 7 months
Contributions summary:Keita primarily contributed to bug fixes and feature enhancements within the PyTorch text processing library. They addressed issues related to the `TabularDataset`, ensuring correct handling of newline characters and implementing the functionality to specify fields using a dictionary. Further work included resolving problems with batch iteration, particularly concerning missing fields, and adding features to the `Field` class, such as stop word filtering. The user's efforts improved data loading and processing capabilities within the library.
nlppytorchloadersdeep-learningdataset
keitakurita/jupyter-slack-notify
Mar 2017 - Feb 2020
A magic command for notifications via slack
Contributions:34 commits, 5 PRs, 39 pushes in 2 years 10 months
slackmagicnotifications
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