Computational Biomechanics Group

Our research group focuses on studying the mechanisms behind initiation and progression of different diseases with a particular focus on cardiovascular disease. Particular attention is given to developing computational models that can capture the multiscale and multiphysics nature of cardiovascular disease. Another key focus of our lab is developing new scientific machine learning models with a broad range of applications. Our work spans a variety of disciplines such as computational fluid dynamics (CFD), computational nonlinear structural mechanics, scientific machine learning, mass transport, dynamical systems, medical imaging, mechanobiology, and multiscale modeling.

Our research centers around the following areas: 1- Fundamental and applied scientific machine learning research. 2- Developing new methods that can model the progression of disease by capturing the multiscale nature of disease growth. 3- Understanding the fundamental blood flow processes in cardiovascular disease. 4- Collaborating with experimentalists to build reliable computational models with hybrid computational-experimental data-driven blood flow modeling using machine learning. 5- Understand flow physics and transport in complex flows with various applications (cardiovascular flows, environmental flows, etc.).

We have dual affiliation with the University of Utah's Scientific Computing and Imaging Institute (SCI Institute) and Mechanical Engineering Department.