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 cutting-age research on AI in medical imaging and biomedical data processing, focusing on how to develop efficient and reliable machine learning (ML) models for biomedical data analysis, towards the goal of multimodal generalist medical 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.