Chloe H. Choi

PhD Candidate, Stanford University

About

I am a fourth year PhD candidate at Stanford University, Department of Mechanical Engineering, advised by Dr. Alison Marsden (Stanford). My research integrates computational fluid dynamics with scientific machine learning and multi-fidelity uncertainty quantification to develop clinically deployable cardiovascular digital twins.

Supported by the AHA predoctoral fellowship, I am collaborating with Dr. Daniele Schiavazzi (University of Notre Dame) and Dr. Jeffrey Feinstein (Stanford) to develop an automated, uncertainty-aware neural network framework that enable efficient and reliable parameter estimation for clinical workflows. These tools will ultimately provide real-time feedback to clinicians to plan difficult treatment procedures for pediatric patients with peripheral pulmonary arterial stenosis (PPAS).

Research interests

• Computational cardiovascular modeling – FEA/CFD, multiscale patient-specific hemodynamics
• Scientific machine learning – point cloud encoders, multi-fidelity physics-informed surrogates
• Uncertainty quantification and inverse problems – Bayesian inference, data assimilation, amortized inference
• Computational biomechanics and scientific computing – cardiovascular mechanics, high-performance computing, numerical methods
• Open-source scientific software – SimVascular, svMultiPhysics, svZeroDSolver, Vascular Model Repository