Don Yang
Software Engineer(QA) at IBM Australia
Greater Sydney Area Australia
Summary
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RockstarDon Yang is a software engineer (QA) based in Greater Sydney with 8 years of engineering experience and a strong focus on machine learning for medical imaging. At IBM Australia he blends QA discipline with hands-on model development and performance engineering, contributing GPU-enabled implementations and profiling enhancements. An active MONAI contributor, he added segmentation models (liver, pancreas), DiNTS blocks, and NVTX annotations to improve training throughput and observability in a leading open-source AI toolkit for healthcare. His work shows a practical mix of experimentation and production-minded optimization—tuning configs, cache/worker settings, and fixing model bugs to make research prototypes robust. Comfortable across ML model implementation, pipeline profiling, and QA practices, he brings an unusual combination of deep-learning engineering and performance troubleshooting to clinical imaging projects.
8 years of coding experience
Github Skills (26)
algorithm10
algorithms10
pytorch10
performance-analytics10
performance-monitor10
code-profiling10
python10
medical-image-processing10
machine-learning10
deeplearning-ai10
performance-analysis10
deep-learning10
ai10
medical-image-computing10
profiling10
Programming languages (2)
Github contributions (5)
Project-MONAI/research-contributions
Sep 2022 - Jan 2023
Implementations of recent research prototypes/demonstrations using MONAI.
Role in this project:
ML Engineer
ML EngineerContributions:65 reviews, 10 commits, 54 PRs in 4 months
Contributions summary:Don primarily contributed to the `project-monai/research-contributions` repository, which focuses on implementations of research prototypes using MONAI, by adding algorithm templates, specifically for DiNTS and SegResNet2d, to the `auto3dseg` module. They updated the scripts involved in searching, training, and inference, which suggests a focus on model development and experimentation. The commits also reveal configurations for automated GPU utilization and the addition of cache rates and worker parameters in the configuration settings.
implementationsmonai-componentsmachine-learningprototypesmonai
Project-MONAI/tutorials
Sep 2021 - Jan 2023
MONAI Tutorials
Role in this project:ML Engineer & Performance Engineer
ML Engineer & Performance EngineerContributions:70 reviews, 13 commits, 34 PRs in 1 year 4 months
Contributions summary:Don contributed to the development of MONAI tutorials, specifically focusing on performance profiling and enhancement of deep learning pipelines. Their work included uploading new scripts, modifying existing ones, and adding version numbers to scripts to improve performance, particularly within the context of NVIDIA Nsight Systems and DLProf. The commits demonstrate hands-on experience with analyzing and optimizing MONAI training pipelines. The user added NVTX annotations to training scripts.
pytorchmonai-tutorialsdeep-learningjupyter-notebookmonai
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