Marisa Schmidt Bazzi

Marisa Schmidt Bazzi

About

I am a Postdoctoral Scholar at Stanford University, Department of Pediatrics, working at the intersection of vascular biomechanics, scientific machine learning, and patient-specific simulation.

My research combines mechanistic modeling — finite element analysis, computational fluid dynamics, fluid–structure interaction, and growth-and-remodeling theory — with data-driven techniques such as constitutive neural networks and symbolic regression. I apply these tools to multimodal clinical data (MRI, IVUS, catheterization) to predict cardiovascular disease progression and guide the design of tissue-engineered vascular grafts.

I contribute to the open-source SimVascular platform and build reproducible Python and C++ workflows for high-performance computing environments.

I earned my Ph.D. in Materials Science from the University of Minnesota under Victor Barocas, and previously trained in Mechanical Engineering at PUC-Rio.

Research interests

  • Vascular biomechanics & growth and remodeling — aneurysm progression, tissue-engineered vascular grafts
  • Scientific machine learning — constitutive neural networks, symbolic regression, physics-informed surrogates
  • Multimodal medical imaging — IVUS, MRI, catheterization-driven simulation pipelines
  • Multiphysics simulation — FEA / CFD / FSI coupling, fluid–solid–growth (FSG) modeling
  • Open-source scientific software — contributions to SimVascular, svFSI, svFSG, svMultiPhysics

Recent news

  • 2026/04 — New paper out in Computers in Biology and Medicine on aortic hemodynamics and Marfan syndrome.
  • 2025 — Selected as a Rising Star in Computational and Data Science (Oden Institute, UT Austin).
  • 2025 — Awarded the CHIP T32 Training Grant.
  • 2024 — Awarded the Baker Postdoctoral Fellowship at Stanford.