Chingis Owana
Senior Machine Learning Engineer at tripla Co., Ltd.
Tokyo, Japan
Summary
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Senior🎓
Top SchoolChingis Owana is a Senior Machine Learning Engineer based in Tokyo with 8 years of experience building production ML systems across search relevancy, representation learning, and debiasing from implicit feedback. He has driven research-to-production work at Mercari and tripla, including counterfactual de-biasing for search ranking and a PEFT fine-tuning strategy for multilingual retrieval in low-resource scenarios. His research background in self-supervised and deep metric learning produced peer-reviewed contributions and practical code—he implemented SubCenterArcFace in the widely used pytorch-metric-learning library. A frequent conference speaker and technical writer, he blends rigorous academic work (Oxford summer school, high honors from Sungkyunkwan) with hands-on pipeline engineering using Kubeflow and Vertex AI. Notably, he iterates between modeling and engineering to ship debiasing and multimodal solutions that improve real-world user experience.
8 years of coding experience
3 years of employment as a software developer
Machine Learning: Summer School, Machine Learning: Summer School at University of OxfordBachelor's degree, Computer Science and Engineering, CGPA: 4.39/4.5 (98.8%), Bachelor's degree, Computer Science and Engineering, CGPA: 4.39/4.5 (98.8%) at 성균관대학교
English, Russian, Kazakh, Korean
Stackoverflow
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Github Skills (6)
computer-vision10
machine-learning10
pytorch10
python10
testing9
deep-learning9
Programming languages (4)
Github contributions (5)
KevinMusgrave/pytorch-metric-learning
Feb 2022 - Feb 2022
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 3 PRs, 6 comments in 9 days
Contributions summary:Chingis contributed to the implementation of the SubCenterArcFace loss function within the `pytorch-metric-learning` library. Their commits involved adding the loss function definition, including the necessary calculations and outlier detection logic. They also updated the testing suite to validate the correct functionality of the newly implemented loss.
pytorchdeep-metric-learningdeep-learningcontrastive-learningcomputer-vision
chingisooinar/AI_self-driving-car
Nov 2021 - Dec 2021
Extensive study and research on Udacity Self-driving Car Challenge 2
Contributions:65 commits, 21 pushes in 29 days
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