Zhaoyi Xu
2632 G.G. Brown Addition
Mechanical Engineering Department
University of Michigan
Ann Arbor, MI 48109
I am currently a PhD student at the University of Michigan , Ann Arbor, under the supervision of Professor Jianping Fu. My research lies at the intersection of biology and engineering, with a focus on understanding human development and health. Specifically, I employ human pluripotent stem cell (hPSC) –based organoids to model early human developmental processes. In addition, I am interested in inverse problems and medical image analysis, with an emphasis on developing interpretable AI models for healthcare applications.
Prior to my doctoral studies, I received my Bachelor of Engineering degree from The Hong Kong University of Science and Technology in 2019 and my Master of Applied Science from the University of Toronto in 2021, supervised by Professor Xinyu Liu. Having lived and studied in Mainland China, Hong Kong, Singapore, Canada, and the United States, I have developed a rich and diverse cultural background, bringing a global perspective that continues to shape and enrich my academic and collaborative work.
research interests
- Human developmental biology
- Human pluripotent stem cell (hPSC) models and therapies
- Interpretable/Reliable AI models for health
news
| Oct 16, 2025 | Our work Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction was accepted to IEEE Transactions on Computational Imaging! |
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| Oct 08, 2025 | Our work Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction was accepted to IEEE Transactions on Computational Imaging! |
| Oct 28, 2024 | I am thrilled to receive the Young Investigator Award at the 8th Bioengineering and Translational Medicine Conference. |
selected publications
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Acoustic cell patterning reveals geometry- and substrate-dependent vasculogenesis and human embryo model development,Nature Communications, under revision -
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI ReconstructionIEEE Transactions on Computational Imaging, Oct 2025