Research

Explore my work. Click on each to see detailed publications and methods.

Multiphysics Modeling and Solver Development

I have developed multiphysics solvers capable of simulating complex cardiovascular behavior. This includes integrating mathematical models of fluid dynamics and vascular solid mechanics into finite-element frameworks and designing novel numerical methods to improve accuracy and computational efficiency. The resulting solvers have been used for translational research and are available as open-source tools for the scientific community.

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Diagram of fluid-solid-growth solver — *A schematic view of the fluid-structure-growth framework where updates are determined by iterative convergence of the multiphysics governing equations.*

Predictive Modeling and Clinical Translation

I collaborate extensively with multi-institutional teams of clinicians and researchers to advance the development of tissue-engineered vascular grafts, using computational models of vascular behavior to predict surgical outcomes and define safety limits for cardiovascular function. These computational tools directly accelerate the clinical translation of new medical technologies.

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Tissue-engineered vascular graft modeling — *A subject-specific tissue-engineered vascular graft (a) was used to create a finite element model (b) and simulate growth and remodeling for 6 week post-operation (c, d).*

Multiscale Modeling and Machine Learning Integration

Artificial intelligence is increasingly ubiquitous in research and medicine. However, proper training and validation of these large-data models are critical for safe adoption in the clinical setting.

I have used multiscale physics-based simulations based on interpretable biological and clinical findings to create high-fidelity data for training neural operators. Such simulations can be varied based on population distributions of vascular geometries, material properties, and hemodynamics to create rich, clinically-relevant datasets at a volume impossible to acquire in vivo. By bridging these biomechanical models with machine learning tools, we can greatly accelerate adoption of artificial intelligence for clinical prediction while maintaining robust standards of data transparency, interpretability, and quality.

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*Architecture of the Laplace neural operator with autoregressive strategy for forecasting thoracic aortic aneurysm dilatation.*