| Description | We are pleased to welcome Shawn Shadden, professor of mechanical engineering at Berkeley, for an ME seminar. About the seminar:
Combining medical imaging and other clinical data with mathematical modeling has become an important paradigm in “digital twin” modeling of cardiovascular dynamics. Such modeling often entails two major steps. The first is development of a patient-specific computer model, and the second is simulating the physiology. This talk will focus on our recent efforts to automate this process using machine learning and reduced-order simulation. Namely, we will describe automated end-to-end deep-learning methods to directly construct personalized models of cardiovascular structures from clinical imaging, as well as reduced order modelling to predict clinically-useful blood flow attributes. About the speaker:
Shawn Shadden is a Professor of Mechanical Engineering at the University of California, Berkeley and a core member of the UCSF-UC Berkeley Graduate Program in Bioengineering. His research focuses on the computational modeling of cardiovascular biomechanics and the advancement of theoretical and numerical methods to quantify complex fluid flow. He is recipient of an NSF CAREER Award, a Bakar Faculty Fellow Award, Hellman Faculty Fellow Award, and the American Heart Association’s Established Investigator Award. His lab helps develop the SimVascular software platform, which is broadly used in the field of computational cardiovascular research. |
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