Daniele E. Schiavazzi

Associate Professor, University of Notre Dame

Daniele E. Schiavazzi

About Me

I am an Associate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, with a concurrent appointment in the Department of Aerospace and Mechanical Engineering. My research focuses on the development of robust and scalable computational methods for uncertainty quantification, data assimilation, machine learning, and agentic scientific computing.

A primary application area of my work is in cardiovascular modeling, where we aim to create patient-specific simulations to improve diagnosis, treatment planning, and medical device design. My lab develops techniques for multi-fidelity surrogate modeling, Bayesian inference, and integrating physics-based models with clinical data from electronic health records and medical imaging.

News

May 2026

Our paper on Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation has been accepted in the Journal of Machine Learning for Modeling and Computing.

March 2026

Our paper on Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction has been accepted in the SIAM Journal of Scientific Computing.

February 2026

Our paper on On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields has been published in Computers in Biology and Medicine.

December 2025

Our paper on On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions has been accepted in Computers in Biology and Medicine.

November 2025

Our paper on NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds has been published in the Journal of Scientific Computing.

Older News

October 2025

Presented our work on multifidelity uncertainty quantification at the SIAM Conference on Mathematics of Data Science in Atlanta, GA.

May 2025

Three new papers accepted in the Philosophical Transactions of the Royal Society A special issue on cardiovascular modeling and uncertainty quantification.

November 2024

New grant awarded from the Notre Dame BELS Initiative for developing LLM agents for physiologic inversion. Joint work with Fang Liu and Nitesh Chawla.

August 2025

Completed visiting scholar position at Sandia National Laboratories, collaborating on advanced UQ methods for computational mechanics.

January 2024

Started visiting scientist position at Brown University, Division of Applied Mathematics.

September 2023

NIH R01 grant awarded for uncertainty-aware virtual treatment planning for pulmonary stenosis in collaboration with Stanford University.

June 2023

Paper on InVAErt networks accepted in Computer Methods in Applied Mechanics and Engineering.

March 2023

Keynote presentation at the SIAM CSE Conference in Amsterdam on multifidelity methods for cardiovascular simulations.

January 2023

New paper published in Journal of Computational Physics on multifidelity data fusion in convolutional encoder/decoder networks.

Honors & Awards

Group

  • Cristian Villatoro

    Data-driven multi-fidelity predictors for physics-based systems

  • Bozhi Sun

    Reinforcement learning for online physics-based surgical decisions

  • Sreejata Dey

    Model synthesis for stiff systems of ODEs

STRAND Undergraduate Students

Testing CAR.L.L., an AI agent for cardiovascular health

Gaaya Binoj, Mckenna Douglas, Elizabeth Hanley, Sara Mietus, Bridget Milligan, Marie Schafer

Alumni

Graduate Alumni

  • Guoxiang (Grayson) Tong

    Machine learning for ensemble physics-based solvers, Deep synthesis for physics-based models

    Current position: Postdoctoral trainee, Stanford University

  • Karlyn Harrod (Jan 2018 - May 2022)

    A non-invasive patient-specific modeling approach for predicting group II pulmonary hypertension as a clinical indicator of diastolic heart failure for patients with uncertain clinical data

    Current position: Oak Ridge National Laboratory

  • Lauren Hensley Partin (Spring 2018 - May 2022)

    Multi-task and multi-fidelity convolutional encoder-decoder networks

    Current position: Siemens Healthineers

  • Xue Li (Spring 2017 - Summer 2021)

    Ensemble finite element solvers for cardiovascular modeling under uncertainty

    Current position: Dalian Maritime University

  • Justin Tran (Fall 2013 - Fall 2018)

    Computational modeling and uncertainty quantification of blood flow in the coronary arteries (co-advised with A. Marsden)

    Current position: Cal State Fullerton

Undergraduate Alumni

  • Luke Lagunowich (November 2023 - May 2026)

    Conditional normalizing flows for forward and backward joint state and parameter estimation

  • John Lee (Summer 2021 - Summer 2024)

    Uncertainty aware virtual treatment planning for pulmonary stenosis in congenital heart disease

  • Hazel Lee (Summer 2022)

    ODE models for the human cardiovascular system

  • John Magargee (Fall 2021)

    Stochastic models for the GameStop trading frenzy: sentiment analysis of reddit posts

  • Shaoyu Liu (Fall 2019)

    Neural network predictors for mispricing of financial assets

  • Yihong Ma (Fall 2018 - Spring 2019)

    Forecast of economic and financial data combining wavelet denoising and artificial neural networks

  • Benjamin Shepard (Fall 2018)

    Introduction to mean field games

  • Jiamin Ge (Fall 2018)

    Multiresolution decomposition of time series with applications in economics and finance

  • Sean Pietrowicz (Fall 2018 - Spring 2019)

    Solenoidal filtering of three-dimensional velocity fields with finite frames

  • Joseph Pennacchio (Summer 2017 - Spring 2018)

    Sparse regression with economic data (co-advised with Prof. Bertanha, Department of Economics)

Publications

Recent Preprints

  1. Dey S., Tong G.G., MacArt J.F., Schiavazzi D.E., Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks, Submitted for publication, 2026.
  2. Choi C.H., Marsden A.L., Schiavazzi D.E., FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions, Submitted for publication, 2026.
  3. Villatoro C.J., Geraci G., Schiavazzi D.E., Assessing the performance of correlation-based multi-fidelity neural emulators, Submitted for publication, 2025.
  4. Sun B., Tierney S., Feinstein J.A., Damen F., Marsden A.L., Schiavazzi D.E., Optimal patient allocation for echocardiographic assessments, Submitted for publication, 2025. [arXiv] [GitHub]

Peer-Reviewed Publications

  1. Lagunowich L.S., Tong G., Schiavazzi D.E., Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation, Accepted, Journal of Machine Learning for Modeling and Computing, 2026.
  2. Lee J., Schiavazzi D.E., On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields, Computers in Biology and Medicine, 205, 2026.
  3. Geraci G., Schiavazzi D.E., Zanoni A., Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction, Accepted, SIAM Journal of Scientific Computing, 2026.
  4. Choi C., Zanoni A., Schiavazzi D.E., Marsden A.L., On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions, Accepted, Computers in Biology and Medicine, 2025.
  5. Zanoni A., Geraci G., Salvador M., Marsden A.L., Schiavazzi D.E., NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds, Journal of Scientific Computing, 105(79), 2025.
  6. Menon K., Zanoni A., Khan O., Geraci G., Nieman K., Schiavazzi D.E., Marsden A.L., Uncertainty-aware coronary hemodynamics personalized by CT perfusion imaging: From Bayesian estimation to improved multi-fidelity uncertainty quantification, Computer Methods and Programs in Biomedicine, 271, 2025.
  7. Tong G.G., Sing Long C.A., Schiavazzi D.E., InVAErt networks for amortized inference and identifiability analysis of lumped parameter hemodynamic models, Philosophical Transactions of the Royal Society A, 383(2293), 2025.
  8. De Florio M., Zou Z., Schiavazzi D.E., Karniadakis G.E., Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology, Philosophical Transactions of the Royal Society A, 383(2292), 2024.
  9. Richter J., Nitzler J., Pegolotti L., Menon K., Biehler J., Wall W.A., Schiavazzi D.E., Marsden A.L., Pfaller M.R., Bayesian Windkessel calibration using optimized 0D surrogate models, Philosophical Transactions of the Royal Society A, 383(2292), 2024.
  10. Zanoni A., Geraci G., Salvador M., Menon K., Marsden A.L., Schiavazzi D.E., Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques, Computer Methods in Applied Mechanics and Engineering, 429(1):117119, 2024.
  11. Schäfer F., Schiavazzi D.E., Hellevik L.R., Sturdy J., Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics, International Journal of Numerical Methods in Biomedical Engineering, 40(8):e3836, 2024.
  12. Lee J.D., Richter J., Pfaller M.R., Szafron J.M., Menon K., Zanoni A., Ma M.R., Feinstein J.A., Kreutzer J., Marsden A.L., Schiavazzi D.E., A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis, International Journal of Numerical Methods in Biomedical Engineering, 40(5):e3820, 2024.
  13. Tong G.G., Sing Long C.A., Schiavazzi D.E., InVAErt networks: a data-driven framework for emulation, inference and identifiability analysis, Computer Methods in Applied Mechanics and Engineering, 423:116846, 2024.
  14. Wang Y., Cobian E.R., Lee J., Liu F., Hauenstein J.D., Schiavazzi D.E., LINFA: a Python library for variational inference with normalizing flow and annealing, Journal of Open Source Software, 2024.
  15. Zanoni A., Geraci G., Salvador M., Menon K., Marsden A.L., Schiavazzi D.E., Linear and nonlinear dimension reduction strategies for multifidelity uncertainty propagation of nonparametric distributions, AIAA SCITECH 2024 Forum, 2024.
  16. Villatoro C., Geraci G., Schiavazzi D.E., On Coordinate Encoding in Multifidelity Neural Networks, AIAA SCITECH 2024 Forum, 2024.
  17. Villatoro C.J., Geraci G., Schiavazzi D.E., On Coordinate Encoding in Multi-Fidelity Neural Emulators, in Computer Science Research Institute Summer Proceedings 2023, Technical Report SAND2023-13916R, Sandia National Laboratories, pp. 422–432, 2023.
  18. Partin L., Schiavazzi D.E., Sing Long C.A., An analysis of reconstruction noise from undersampled 4D flow MRI, Biomedical Signal Processing and Control, 84:104800, 2023.
  19. Cobian E.R., Hauenstein J.D., Liu F., Schiavazzi D.E., AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation, International Journal of Uncertainty Quantification, 13(3):39-68, 2023.
  20. Tong G.G., Schiavazzi D.E., Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue, Computational Mechanics, 71(3):453-479, 2023.
  21. Partin L., Geraci G., Rushdi A.A., Eldred M.S., Schiavazzi D.E., Multifidelity data fusion in convolutional encoder/decoder networks, Journal of Computational Physics, 472:111666, 2023.
  22. Wang Y., Liu F., Schiavazzi D.E., Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models, Journal of Computational Physics, 467:111454, 2022.
  23. Li X., Schiavazzi D.E., An ensemble solver for segregated cardiovascular FSI, Computational Mechanics, 68, 1421-1436, 2021.
  24. Maher G.D., Fleeter C.M., Schiavazzi D.E., Marsden A.L., Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks, Computer Methods in Applied Mechanics and Engineering, 386, 114038, 2021.
  25. Harrod K.K., Rogers J.L., Feinstein J.A., Marsden A.L., Schiavazzi D.E., Predictive modeling of secondary pulmonary hypertension in left ventricular diastolic dysfunction, Frontiers in Computational Physiology, 12, 654, 2021.
  26. Seo J., Fleeter C.M., Kahn A.M., Marsden A.L., Schiavazzi D.E., Multi-fidelity estimators for coronary circulation models under clinically-informed data uncertainty, International Journal for Uncertainty Quantification, 10(5):449-466, 2020.
  27. Seo J., Schiavazzi D.E., Kahn A.M., Marsden A.L., The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls, International Journal For Numerical Methods In Biomedical Engineering, 36(8), 2020.
  28. Fleeter C.M., Geraci G., Schiavazzi D.E., Kahn A.M., Marsden A.L., Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics, Computer Methods in Applied Mechanics and Engineering, 365, 2020.
  29. Khosravi R., Bangalore Ramachandra A., Szafron J.M., Schiavazzi D.E., Breuer C.K., Humphrey J.D., A computational Bio-Chemo-Mechanical model of in-vivo tissue engineered vascular graft development, Integrative Biology, 12(3):47-63, 2020.
  30. Akintunde A.R., Miller K.S., Schiavazzi D.E., Bayesian inference of constitutive model parameters from uncertain uniaxial experiments on murine tendons, Journal of the Mechanical Behavior of Biomedical Materials, 96:285-300, 2019.
  31. Seo J., Schiavazzi D.E., Marsden A.L., Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels, Computational Mechanics, 64(3):717-739, 2019.
  32. Tran J.S., Schiavazzi D.E., Kahn A.M., Marsden A.L., Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts, Computer Methods in Applied Mechanics and Engineering, 345:402-428, 2019.
  33. Amili O., Schiavazzi D.E., Moen S., Jagadeesan B., Van de Moortele P.F., Coletti F., Hemodynamics in a giant intracranial aneurysm characterized by in vitro 4D flow MRI, PLOS One, 13(1), 2018.
  34. Schiavazzi D.E., Nemes A., Schmitter S., Coletti F., The Effect of Velocity Filtering in Pressure Estimation, Experiments in Fluids, 58(5):50, 2017.
  35. Schiavazzi D.E., Doostan A., Iaccarino G., Marsden A.L., A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling, Computer Methods in Applied Mechanics and Engineering, 314:196-221, 2017.
  36. Ward E., Schiavazzi D.E., Sood D., Marsden A., Lane J., Owens E., Barleben A., CT FFR Can Identify Culprit Lesions in Aorto-iliac Occlusive Disease Using Minimally-Invasive Techniques, Annals of Vascular Surgery, 38:151-157, 2017.
  37. Tran J., Schiavazzi D.E., Ramachandra B.A., Kahn A., Marsden A.L., Automated tuning for parameter identification in multi-scale coronary simulations, Computers & Fluids, 142(5):128-138, 2017.
  38. Schiavazzi D.E., Baretta A., Pennati G., Hsia T.Y., Marsden A.L., Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty, International Journal of Numerical Methods in Biomedical Engineering, 33(3):e02799, 2017.
  39. Schiavazzi D.E., Hsia T.Y., Marsden A.L., On a sparse pressure-flow rate condensation of rigid circulation models, Journal of Biomechanics, 49(11):2174-2186, 2016.
  40. Schiavazzi D.E., Arbia G., Baker C., Hsia T.Y., Marsden A.L., Vignon-Clementel I.E., Uncertainty quantification in virtual surgery hemodynamics predictions for single ventricle palliation, International Journal of Numerical Methods in Biomedical Engineering, 32(3), 2016.
  41. Schiavazzi D.E., Kung E., Marsden A.L., Baker C., Pennati G., Hsia T.Y., Hlavacek A.M., Dorfman A.L., Hemodynamic effects of left pulmonary artery stenosis following superior cavopulmonary connection: a patient-specific multiscale modeling study, Journal of Thoracic and Cardiovascular Surgery, 149(3):689-696, 2015.
  42. Banko A.J., Coletti F., Schiavazzi D.E., Elkins C.J., Eaton J.K., Three-dimensional inspiratory flow in the upper and central human airways, Experiments in Fluids, 56(6):117, 2015.
  43. Schiavazzi D.E., Doostan A., Iaccarino G., Sparse multiresolution regression for uncertainty propagation, International Journal for Uncertainty Quantification, 4(4):303-331, 2014.
  44. Schiavazzi D.E., Coletti F., Iaccarino G., Eaton J.K., A matching pursuit approach to solenoidal filtering of three-dimensional velocity measurements, Journal of Computational Physics, 263:206-221, 2014.
  45. Coletti F., Muramatsu K., Schiavazzi D.E., Elkins C.J., Eaton J.K., Fluid flow and scalar transport through porous fins, Physics of Fluids, 26(5):055104, 2014.

Book Chapters

  1. Choi C., Zanoni A., Schiavazzi D.E., Marsden A.L., A primer on uncertainty quantification for cardiovascular simulations, in Model Validation and Uncertainty Quantification in Biomechanics of Soft Tissues edited by Gerhard Holzapfel, Malte Rolf-Pissarczyk, Xiao Yun Xu, 2025.
  2. Schäfer F., Choi C., Zanoni A., Menon K., Geraci G., Marsden A.L., Schiavazzi D.E., An introduction to multi-fidelity uncertainty propagation for applications in biomechanics, in Model Validation and Uncertainty Quantification in Biomechanics of Soft Tissues edited by Gerhard Holzapfel, Malte Rolf-Pissarczyk, Xiao Yun Xu, 2025.

Conference Proceedings

  1. Partin L., Rushdi, A.A., Schiavazzi D.E., Multifidelity data fusion in convolutional encoder/decoder assembly networks for computational fluid dynamics, Proceedings of the AIAA Scitech 2022 Forum, 3-7 January 2022, San Diego, CA, USA.
  2. Schiavazzi D.E., Juliano T.J., Bayesian network inference of thermal protection system failure in hypersonic vehicles, Proceedings of the AIAA Scitech 2020 Forum, 6-10 January 2020, Orlando, FL, USA.
  3. Schiavazzi D.E., Fleeter C.M., Geraci G. and Marsden A.L., Multifidelity uncertainty propagation for cardiovascular hemodynamics, Proceedings of the 6th European Conference on Computational Mechanics (ECCM 6) and 7th European Conference on Computational Fluid Dynamics (ECFD 7), 11-15 June 2018, Glasgow, UK.
  4. Schiavazzi D., Doostan A. and Iaccarino G., Sparse multiresolution stochastic approximation for uncertainty quantification, Recent Advances in Scientific Computing and Applications, 586 pp. 295-303, AMS 2013.

Technical Reports

  1. Partin L., Geraci G., Rushdi A.A., Eldred M.S., Schiavazzi D., Multifidelity Data Fusion in Convolutional Encoder/Decoder Assembly Networks for Computational Fluid Dynamics Applications, in Center for Computing Research Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2021-15925R, Sandia National Laboratories, pp. 102-119, 2021.
  2. Fleeter C.M., Geraci G., Schiavazzi D., Kahn A.M., Eldred M.S., Marsden A.L., Multifidelity multilevel approaches for cardiovascular flow under uncertainty, in Center for Computing Research Summer Proceedings 2017, A.D. Baczewski and M.L. Parks, eds., Technical Report SAND2018-2780O, Sandia National Laboratories, pp. 27-50, 2018.
  3. Tachikawa H., Schiavazzi D., Arima T., Iaccarino G., Robust optimization for windmill airfoil design under variable wind conditions, Honda R&D Technical Review, 26(1):160-165, 2014.
  4. Tachikawa H., Schiavazzi D., Arima T., Iaccarino G., Robust optimization for windmill airfoil design under variable wind conditions, Proceedings of the 2012 CTR Summer Program, Stanford University.
  5. Lucor D., Witteveen J., Constantine P., Schiavazzi D., Iaccarino G., Comparison of adaptive uncertainty quantification approaches for shock wave-dominated flows, Proceedings of the 2012 CTR Summer Program, Stanford University.
  6. Schiavazzi D., Doostan A., Iaccarino G., A Sparse Multiresolution Stochastic approximation for uncertainty quantification, CTR Research Briefs 2012, Stanford University.
  7. Schiavazzi D., Coletti F., Iaccarino G., Eaton J.K., A matching pursuit approach to solenoidal filtering of three-dimensional velocity measurements, CTR Research Briefs 2012, Stanford University.

Teaching

ACMS 40760 - Introduction to Stochastic Modeling

Instructor, University of Notre Dame

Fall 2016, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Fall 2020, Spring 2021, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Fall 2024

Project Collection: Explorations in Stochastic Modeling - Fall 2024

Project Collection: Explorations in Stochastic Modeling - Fall 2023

ACMS 60792 - Cardiovascular Modeling and Uncertainty Quantification

Instructor, University of Notre Dame

Spring 2025, Spring 2026

Project Collection: Explorations in Cardiovascular Modeling and Uncertainty Quantification - Spring 2025

STRAND Undergraduate Research - Cardiovascular Modeling

Supervised a team of five undergraduate students through weekly meetings, focusing on the creation of cardiovascular models, application of physiological boundary conditions, numerical solution and post-processing with VTK scripting.

Fall 2024, Spring 2025, Fall 2025, Spring 2026, Fall 2026

More Teaching

ACMS 40390 - Numerical Analysis

Instructor, University of Notre Dame

Spring 2022

ACMS 70860 - Stochastic Analysis

Instructor, University of Notre Dame

Spring 2017, Spring 2019

ACMS 40730 - Mathematical/Computational Modeling

Instructor, University of Notre Dame

Fall 2017, Fall 2024

Project Collection: Explorations in Mathematical and Computational Modeling - Fall 2024

ACMS 80770 - Topics in Applied Mathematics

UQ for Computational Science and Engineering: Monte Carlo and spectral stochastic methods. Instructor, University of Notre Dame

Fall 2017

ME 469 - Computational Methods in Fluid Mechanics

Teaching Assistant, Stanford University

Fall 2012-2013

MAE 290B - Numerical Methods for Differential Equations

Guest Lecturer, University of California, San Diego

Winter 2013-2014