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

All are invited to the Frederica Darema Lecture Series on Monday, April 28, 2025, from 12:45–1:45 p.m. at the Stuart Building, room 111. Liyue Shen, assistant professor of EECS, University of Michigan, will give a talk titled “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.

Biography

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.

About the Frederica Darema Lecture Series

The Illinois Institute of Technology College of Computing’s Dr. Frederica Darema Lecture Series in Computer Science is funded by an endowment to help advance female and minority early-stage computer science researchers at U.S. academic institutions.

The lecture series is designed to encourage women and individuals from under-represented groups to pursue academic careers in computer sciences, and to focus on providing speaking opportunities for tenure track assistant professors (or the equivalent) at U.S. institutions in their fourth to sixth year. Lectureships may also be awarded to exceptional junior researchers in U.S. federal or industrial research laboratories in the third to fifth years of their careers, following doctoral/postdoctoral studies.