Bio

I am a research data scientist in Stanford's Institute for Human-Centered Artificial Intelligence. I completed my PhD within Stanford's Institute of Computational and Mathematical Engineering (ICME), with my dissertation work under Dr. Alison Marsden focusing on applications of mathematical models and uncertainty quantification for the cardiovascular system. Prior to returning to Stanford, I was employed as a postdoctoral researcher at Calico Life Sciences, where I focused on mechanistic models of aging and aging related disease. I graduated from Harvard University with a Bachelor of Arts in Physics.

Academic Appointments

  • Research Engineer, Other Academic Support

Honors & Awards

  • EDGE Fellow, Stanford University (2015-Present)

Boards, Advisory Committees, Professional Organizations

  • Member, SIAM (2015 - Present)
  • Member, APS (2017 - Present)

Professional Education

  • MS, Stanford University, Computational and Mathematical Engineering (2018)
  • AB, Harvard University, Physics (2015)

Links

All Publications

  • Geometric uncertainty in patient-specific cardiovascular modeling with convolutional dropout networks COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING Maher, G. D., Fleeter, C. M., Schiavazzi, D. E., Marsden, A. L. 2021; 386

    View details for DOI 10.1016/j.cma.2021.114038

    View details for Web of Science ID 000703985200002

  • Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics. Computer methods in applied mechanics and engineering Fleeter, C. M., Geraci, G., Schiavazzi, D. E., Kahn, A. M., Marsden, A. L. 2020; 365

    Abstract

    Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

    View details for DOI 10.1016/j.cma.2020.113030

    View details for PubMedID 32336811

  • Multi-fidelity estimators for coronary artery circulation models under clinically-informed data uncertainty International Journal for Uncertainty Quantification Seo, J., Fleeter, C., Kahn, A. M., Marsden, A. L., Schiavazzi, D. E. 2020

    View details for DOI 10.1615/Int.J.UncertaintyQuantification.2020033068