Summary
Shuyue Guan is a Staff Fellow at the FDA and an adjunct professor at George Washington University with nine years of experience applying image processing, machine learning, and deep learning to medical imaging problems. Her research focuses on data separability measures and model learnability to make deep learning more transparent and reliable for clinical applications. She has progressed through multiple research fellow roles at the FDA, contributing to translational projects that bridge regulatory science and algorithmic validation. Academically she holds a PhD in Biomedical Engineering and a Master's in Computer Science, and she has taught courses in pattern recognition and computer vision. Beyond conventional modeling, she brings hands-on experience with hyperspectral imaging for cardiac lesion detection, highlighting a niche expertise in specialized imaging modalities. Based in the Washington DC–Baltimore area, she combines rigorous academic training with practical regulatory research to advance trustworthy AI in healthcare.
9 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Biomedical/Medical Engineering, Doctor of Philosophy - PhD, Biomedical/Medical Engineering at The George Washington University
Master's degree, Biophysics, Master's degree, Biophysics at Northeast Forestry University
Chinese, English, Japanese