Vijay Vedula, Ph.D.
Department of Mechanical Engineering
Columbia University in the City of New York
Seminar Information
The synergy between advances in medical imaging and simulation methodologies, including multiscale and multiphysics models of cardiovascular function, data-driven models, and machine learning, is enabling computational modeling as an indispensable tool for studying cardiovascular biomechanics in health and disease, which remains the leading cause of worldwide deaths (1 in 3). In this talk, I will present our recent work leveraging these advances for developing personalized models of cardiac mechanics, particularly for a biventricle and the left atrium. We will then discuss how the model can be used to understand the role of anatomy in informing the modeling choices, before discussing the challenges and our approach when applying this framework to a pathological case. We will revisit our modeling assumptions on the material properties, supported by preliminary mechanical testing data, and introduce a novel machine learning framework to overcome the computational cost associated with personalizing passive mechanics. Finally, drawing parallels between cardiac and uterine mechanics, we will discuss how the current multiphysics modeling framework could be applied to study pregnancy.
Dr. Vijay Vedula is an Assistant Professor in the Mechanical Engineering department at Columbia University, where he directs the Cardiovascular Biomechanics Research Lab (CBRL). He began his academic training in India (Bachelor’s in Mechanical Engineering from NIT Trichy, followed by Master’s in Aerospace Engineering from IIT Kanpur) and continued to obtain a Ph.D. in Mechanical Engineering at Johns Hopkins University. He then underwent postdoctoral training with Prof. Alison Marsden at UCSD and Stanford. His lab’s research is currently funded by AHA, NIH, and NSF, and he is a recipient of multiple awards and fellowships, including a postdoctoral fellowship from the Child Health Research Institute at Stanford University, a von Karman visitor fellowship at RWTH Aachen University, an Early Faculty Independence Award from the American Heart Association (AHA SCEFIA), and more recently, the NSF CAREER award. His research interests encompass cardiovascular biomechanics and computation, computational fluid dynamics, fluid-structure interaction, and, more recently, inverse problems and data-driven modeling for personalization.