Integrating Multiscale modeling and Machine Learning In Computational Medicine
Thursday March 6, 2024 from 12:00-1:00pm
Virtual Teams Presentation
Computational models have revolutionized our understanding of complex biological systems. These models typically fall into two categories: multiscale models and machine learning models. Multiscale models, grounded in the fundamental principles of physics and chemistry, can dissect the intricate causalities in disease progression. In parallel, machine learning models, based on rich datasets, have yielded unique insights into complex pattern recognition within these systems.
In this talk, Dr. Deng will demonstrate how multiscale models are developed to probe disease-mediated changes in blood dynamics. She will also show how various machine learning models, with a focus on deep learning, are designed to enhance accurate prediction, optimize treatment plans, and distill extensive knowledge of different diseases.
To conclude, she will discuss the pivotal factors—such as age and sex—that are critical to customizing treatments in the realm of precision medicine, and to synergistically integrate multiscale modeling with machine learning to enable the design of more comprehensive personalized medicine.
Meet the Speaker
Yixiang Deng, Ph.D.
Assistant Professor, Computer & Information Sciences University of Delaware
Yixiang Deng is an assistant professor at the University of Delaware in the Department of Computer and Information Sciences. She has worked as a Postdoctoral Fellow at the Ragon Institute of Mass General, MIT, and Harvard, with a joint appointment at the MIT Department of Biological Engineering. Her research focuses on integrating multiscale modeling and machine learning to probe the mechanisms of diseases and the effectiveness of therapeutics. Before joining the Ragon Institute, she completed her Ph.D. in the School of Engineering at Brown University in 2021 and a B.Eng. degree in Engineering Mechanics at Shanghai Jiao Tong University.
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Please RSVP to Debra.Reese@ChristianaCare.org Include your name, email address, and institution/organization. We will provide instructions on obtaining CME credit for attendance.
Work supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number U54-GM104941 (PI:Hicks) and the State of Delaware.