Sophie Aminu
Data Scientist
Fukuoka Prefecture, Japan
Summary
🤩
Rockstar🎓
Top SchoolSophie Aminu is a data scientist and ML systems engineer with over five years of hands-on experience building production-ready software and machine learning systems across Japan. Trained as a mechanical engineer at the University of Surrey and with research exposure in mechatronics and robotics, she bridges physical systems thinking with practical backend ML engineering. Her open-source work includes backend contributions to the ivy library—improving Array API compatibility across PyTorch, TensorFlow, JAX and NumPy—demonstrating attention to standards and cross-backend robustness. Bilingual in English and Japanese (JLPT N1) and based in Fukuoka, she thrives in multicultural engineering teams and has moved between startups, consulting, and research roles to deliver scalable ML solutions.
5 years of coding experience
5 years of employment as a software developer
2nd Year Study Abroad, Mechanical Engineering, 2nd Year Study Abroad, Mechanical Engineering at Seoul National UniversityUrsuline High School, Wimbledon日本語教育プログラム, 日本語教育プログラム at Waseda UniversityBachelor of Engineering - BE, Mechanical Engineering, Bachelor of Engineering - BE, Mechanical Engineering at University of SurreyResearch Student, Mechatronics, Robotics, and Automation Engineering, Research Student, Mechatronics, Robotics, and Automation Engineering at Kyushu University
Github Skills (11)
ivy10
python10
back-end-development10
numpy10
jax9
tensorflow9
machine-learning9
pytorch9
deep-learning8
deeplearning-ai8
converter8
Programming languages (5)
Github contributions (5)
ivy-llc/ivy
Mar 2022 - Aug 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
Back-end Developer
Back-end DeveloperContributions:102 reviews, 16 commits, 56 PRs in 4 months
Contributions summary:Sophie primarily contributed to the project by implementing and modifying functions within the `ivy` library to align with the Array API Standard. Their work involved adding support for new functions like `positive`, and ensuring the correct implementation across various backends, including PyTorch, NumPy, TensorFlow, and JAX. They also made adjustments to the data types used, and refactored and updated backend-specific function implementations. The user's changes improve the library's compatibility and adherence to standards.
pythontensorflowframework-learningtemplatedata-science
chie2727/ivy
Mar 2022 - Jun 2022
The Unified Machine Learning Framework
Contributions:32 pushes, 10 branches in 2 months
pythondata-sciencedeep-learningmachine-learningframework-learning
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