Shoukun Sun is a PhD-level AI researcher and data scientist with 11 years of engineering experience, specializing in sparse-label learning, computer vision, and medical/remote-sensing imaging. At the University of Idaho he built GPU-backed annotation platforms, led a state-of-the-art interactive segmentation model that cut annotation clicks by 33.2% and achieved a global benchmark #1, and developed training-free text-to-image diffusion methods that reduced FID by 64%. He combines hands-on systems work—managing a 15×RTX GPU cluster and building end-to-end annotation and NLP pipelines—with strong publication impact (10 papers, 4 first-author at AAAI/MICCAI/Scientific Reports). Formerly a developer in web and HPC contexts, he brings practical production skills (Python, PyTorch, Docker, AWS, SLURM) to research problems, enabling interdisciplinary teams to scale data labeling and model training. Based in California, he is particularly effective at turning limited-data challenges into deployable AI solutions for healthcare and materials science.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Idaho
Bachelor's degree, Engineering Physics/Applied Physics, Bachelor's degree, Engineering Physics/Applied Physics at Chengdu University of Technology
Contributions:108 pushes, 3 branches in 2 years 4 months
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