Yuan-ting Hsieh
Senior Software Engineer at NVIDIA
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYuan-ting Hsieh is a Senior Software Engineer in San Jose with 10 years of experience bridging machine learning research and production-grade AI infrastructure. With an MS in Computer Science from UW–Madison and a BS from NTU, he has applied classical and deep learning methods across NLP, computer vision, and recommendation systems, producing peer-reviewed work in domain adaptation. At NVIDIA he contributes to NVFlare (federated learning runtime) and MONAI (widely used medical imaging toolkit), demonstrating strengths in backend systems, distributed training, and ML tooling. He is proficient in C++ and object-oriented design and has a track record of improving usability and robustness through documentation, test coverage, and API enhancements. Colleagues value his ability to translate research ideas into scalable implementations—an underappreciated skill reflected in both his open-source fixes and clinical AI deployment experience.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Electrical engineering, GPA: 3.83/4.00, Bachelor of Science - BS, Electrical engineering, GPA: 3.83/4.00 at National Taiwan UniversityMaster of Science - MS, Computer Science, cGPA: 3.90/4.0, Master of Science - MS, Computer Science, cGPA: 3.90/4.0 at University of Wisconsin-Madison
English, Chinese, Mandarin
Stackoverflow
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Github Skills (16)
medical-imaging10
pytorch10
deeplearning-ai10
deep-learning10
python10
imaging10
medical-image-processing10
documentation10
api-design9
federated-learning9
mlops9
distributed-training8
scikit-image8
tensorboard8
testing8
Programming languages (5)
Github contributions (5)
NVIDIA/NVFlare
Dec 2021 - Jan 2023
NVIDIA Federated Learning Application Runtime Environment
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3505 reviews, 233 commits, 1456 PRs in 1 year 2 months
Contributions summary:Yuan-ting primarily focused on enhancing the functionality and documentation of existing components within the NVIDIA FLARE project. They added docstrings to various widgets, including the `InfoCollector`, `TBAnalyticsReceiver`, `ComponentCaller`, `ConvertToFedEvent`, and `Streaming` widgets. The changes involved adding explanations for component behavior and updating internal API specifications. The user also introduced the `LearnerServiceProviderSpec` and updated the `AnalyticsSender` to handle more data types.
pythonnvidiadeep-learningruntimemachine-learning
Project-MONAI/MONAI
May 2020 - Mar 2021
AI Toolkit for Healthcare Imaging
Role in this project:ML Engineer
ML EngineerContributions:42 reviews, 13 commits, 18 PRs in 9 months
Contributions summary:Yuan-ting contributed significantly to the MONAI project, an AI toolkit for healthcare imaging, primarily focused on enhancing the functionality of the `KeepLargestConnectedComponent` transform and the overall deep learning workflow. They added one-hot support and improved test coverage for this transform, demonstrating a solid understanding of image processing techniques. The user also added a distributed training example for the BRATS dataset, showcasing their experience with distributed training frameworks and model deployment. Furthermore, the user addressed networking errors for pre-trained weights, and fixed typos and accuracy calculations, demonstrating a commitment to code quality and usability.
pytorchpythondeep-learningmedical-image-processingcomputer-vision
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