Liyue Shen is an assistant professor in the ECE Division of the Electrical Engineering and Computer Science Department of the College of Engineering, University of Michigan – Ann Arbor. She is also affiliated with Michigan Institute for Data Science (MIDAS), and Michigan Institute for Computational Discovery and Engineering (MICDE).
She received her B.E. degree in Electronic Engineering from Tsinghua University in 2016, and obtained her Ph.D. degree from the Department of Electrical Engineering, Stanford University in 2022, co-advised by Prof. John Pauly and Prof. Lei Xing. She was a postdoctoral research fellow at the Department of Biomedical Informatics, Harvard Medical School from 2022 to 2023. She is the recipient of Stanford Bio-X Bowes Graduate Student Fellowship (2019-2022), and was selected as the Rising Star in EECS by MIT and the Rising Star in Data Science by The University of Chicago in 2021.
Her research interest is in Biomedical AI, which lies in the interdisciplinary areas of machine learning, computer vision, signal and image processing, medical image analysis, biomedical imaging, and data science. She is particularly interested in developing efficient and reliable AI/ML-driven computational methods for biomedical imaging and informatics to tackle real-world biomedicine and healthcare problems, including but not limited to, personalized cancer treatment, and precision medicine.
She co-organized the Woman in Machine Learning (WiML) workshop at ICML’ 21 and NeurIPS’ 23, and the Machine Learning for Healthcare (ML4H) workshop at NeurIPS’ 21. In MICCAI’ 21, she co-taught the tutorial on Deep 2D-3D Modeling and Learning in Medical Image Computing.
- Invited to give a talks at UMich Dept. of Learning Health Sciences (DLHS) Research Seminar about AI for Medical Imaging. (Nov. 2023)
- Received a grant of 2023 MICDE Catalyst Grant from Michigan Institute for Computational Discovery and Engineering (MICDE) in collaboration with Prof. Jeffrey A. Fessler and Prof. Qing Qu, for supporting our research on Efficient Diffusion Models for Scientific Machine Learning. (Oct. 2023)
- Release our new paper on arXiv: The Emergence of Reproducibility and Consistency in Diffusion Models. (Oct. 2023)
- Release our new paper on arXiv: Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior. (Sep. 2023)
- Invited to give talks at ICCV 2023 Computer Vision for Automated Medical Diagnosis (CVAMD) Workshop, UMich MIDAS mini-symposium on AI for Medical Imaging, UMich ECE Communications and Signal Processing Seminar about our recent work using latent diffusion models for solving general inverse problems. Please check the video recording. (Sep. 2023)
- Organize research symposium at U-M MIDAS: Generative AI: Diffusion Models for Scientific Machine Learning. (Sep. 15th, 2023)
- Release our new paper on arXiv: Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. (Jul. 2023)
- Received a grant of Propelling Original Data Science (PODS) 2023 award from Michigan Institute for Data Science (MIDAS) in collaboration with Prof. Lise Wei (UMich Medicine), for supporting our collaborative research on personalized progression risk prediction for cancer patients from longitudinal multi-modality data. (Jun. 2023)
- Organization committee for a new conference: Conference on Parsimony and Learning (CPAL), which will be held in Jan. 2024.