Originally from Germany, Dr. Rausch earned his PhD from Stanford University in 2013 before taking on the role of Director of R&D at a small medical device company. After a two year stint in industry, Dr. Rausch returned to academia as a post-doctoral fellow at Yale University. As of 2017, Dr. Rausch is an assistant professor in the Department of Aerospace Engineering & Engineering Mechanics and the Department of Biomedical Engineering, within the Cockrell School of Engineering.
He is the principal investigator in the Soft Tissue Biomechanics Laboratory.
Dr. Rausch's research interests are focused on soft tissue biomechanics. He uses experimental as well as computational tools to characterize and understand the mechanical behavior of biological soft tissues such as myocardium, vascular soft tissue, heart valve tissue, and skin to improve diagnostic and therapeutic methods, and medical device design. ASE 5.236
In May of 2020, he received the Moncrief Grand Challenge Award for his proposal titled ‘A Machine-Learning Based Training Tool for Tricuspid Valve Repair: A Prototype’. The objective of this proposal is to develop a prototype learning tool that incorporates all complexities of a human tricuspid valve and provides in-depth didactic insight into the effects of repair and device implantation on valve function. The outcome of this project will be a machine-learning based surrogate model that has been trained via high-fidelity finite element simulations. The finite element model itself will be built around a detailed cadaver study that includes all valvular and sub-valvular complexities. The surrogate educational model will be able to visualize the kinematics (i.e., competence) of the valve at minimal computational cost in comparison to a full simulation. Thus, the user will be able to change key valve parameters and learn their effect on valve function near instantaneously. This prototype will be a showcase for the potential of machine-learning based virtual training tools. It thereby holds the promise of aiding clinical training and reducing training-related morbidity and mortality.