Frederica Darema Lecture Series by Liyue Shen: AI for Medical Imaging and Bioinformatics

Speaker: Liyue Shen, assistant professor of EECS, University of Michigan

 

Title: AI for Medical Imaging and Bioinformatics 

 

Abstract:  

Deepening our understanding of human health is more important than ever before to address real-world challenges in biomedicine and healthcare. In this talk, I will introduce the cutting-edge AI research developed for medical imaging and biomedical data processing, focusing on how to develop efficient and reliable machine learning (ML) models for biomedical data to address real-world challenges. The first part of the talk will introduce recent advancements in our works to enhance the efficiency of diffusion-based generative models for solving general inverse problems via posterior sampling. We present innovative approaches, including latent diffusion and patch diffusion models, designed to learn diffusion priors for high-dimensional, high-resolution image reconstruction. In the second part, we delve into our recent study on multi-modal, multi-task learning in biomedical data. Our research introduces novel insights into dynamic modeling across modalities, tasks, and patients, showcasing advancements in multimodal biomedical AI. 

 

Bio: Liyue Shen is an Assistant Professor in the EECS department at the University of Michigan. Prior to that, 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. She also spent one year as a postdoctoral research fellow at the Department of Biomedical Informatics, Harvard Medical School. Her research interest is in Biomedical AI, which lies in the interdisciplinary areas of machine learning, computer vision, signal and image processing, biomedical imaging, medical image analysis, and data science. She recently focuses on the generative diffusion models, implicit neural representation learning and multimodal foundation models. 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. She serves as area chairs for ICLR, MLHC, and helps organize multiple conferences and workshops including CPAL, ISBI, WiML, ML4H.

 

Learn more about the Frederica Darema Lecture Series