Mingzhen Shao is a PhD candidate and applied researcher with 9 years of experience building and deploying computer vision and deep learning systems across industry and academia. He has driven production-scale vision improvements—from a multi-camera fusion algorithm shipped to over three million devices to a real-time, DL-based head-pose estimator for Microsoft Face SDK—while now focusing on explainability, steerability, generalization, and robustness in medical imaging. At the University of Utah he designed an automated histology-to-MR data pipeline that tripled data quality and is developing models to non-invasively correlate MR and H&E for breast tumor treatment. Comfortable bridging research and product engineering, he has collaborated with teams like Qualcomm to optimize ISP toolchains and repeatedly turned complex imaging problems into deployable solutions. Based in Salt Lake City, he blends rigorous academic training from Tohoku and Shandong with hands-on system delivery and a curiosity for model properties beyond raw performance.
9 years of coding experience
2 years of employment as a software developer
The University of Utah
Master of Engineering - MEng, Image Analize, Deep Learning, Master of Engineering - MEng, Image Analize, Deep Learning at Tohoku University
Bachelor of Engineering (B.E.), Automation Engineer Technology/Technician, Bachelor of Engineering (B.E.), Automation Engineer Technology/Technician at Shandong University
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