Sangbum Choi is a grad student and experienced software engineer with eight years of hands-on experience in machine learning and systems development, based in Madison, Wisconsin. He contributes to high-impact open-source projects like Hugging Face Transformers, where he has improved DETR-based object detection and segmentation models, fixed auxiliary loss issues, implemented gradient checkpointing, and integrated custom CUDA kernels for performance. Comfortable bridging research and production, he focuses on model reliability, training stability, and code quality while iterating on cutting-edge vision architectures. Known for a methodical, debate-driven approach to problem solving, he values consistency and continuous learning, which shows in both academic work and upstream contributions. Sangbum combines deep technical skill with an eye for practical optimizations that make large models train and run more efficiently.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Role in this project:
ML Engineer
Contributions:321 reviews, 26 PRs, 520 comments in 1 year 9 months
Contributions summary:Sangbum primarily contributed to the development and improvement of machine learning models within the Hugging Face Transformers library. Their work included implementing and debugging features related to DETR-based object detection models, such as Deta, Conditional DETR, and Deformable DETR, and the Mask2Former/MaskFormer segmentation models. They focused on fixing auxiliary loss training and gradient checkpointing, as well as improving model training and code quality, and integrating custom CUDA kernels for performance optimization.
Contributions:140 commits, 11 pushes, 3 branches in 1 year 7 months
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