Dr. Amir Arzani received his BS from Isfahan University of Technology (2010), MS from Illinois Institute of Technology (2012), and PhD from University of California Berkeley (2016) all in Mechanical Engineering. He was a postdoc for one year at UC Berkeley's Bioengineering Department and prior to joining SCI he was an Assistant Professor at Northern Arizona University (NAU) Mechanical Engineering Department for 5 years where he directed the Cardiovascular Biomechanics Lab. He is a recipient of the NSF CAREER Award.
Dr. Arzani's group utilizes modern scientific computing tools for modeling, processing, and fundamental understanding of unsteady fluid flow problems with a particular emphasis on cardiovascular flows and image-based blood flow modeling. More recently, his group has focused on scientific machine learning approaches (physics-informed machine learning and sparse data-driven modeling) for modeling blood flow.
- Scientific Machine learning
- Data-driven reduced-order modeling
- Computational fluid dynamics (CFD)
- Computational structural mechanics
- Mass transport
- Dynamical systems
- Cardiovascular biomechanics