Short Bio

I'm an Assistant Professor in the ACMS Department, University of Notre Dame. I graduated with honors and received a Ph.D. degree in Applied Mathematics from Universita' degli Studi di Padova, Italy. I have completed my Ph.D. thesis as a Visiting Researcher at Stanford University, followed by a Postdoctoral position at University of California, San Diego and Stanford University. My main research interests are in stochastic analysis, multi-resolution approximation, numerical modeling and finite element analysis, adaptive Markov chain Monte Carlo estimation and use of computational models to inform clinical decision making under uncertainty.


Ph.D. in Applied Mathematics - Universita' degli Studi di Padova.
M.Sc. in Civil/Structural Engineering - Universita' degli Studi di Padova.
B.Sc. in Civil/Structural Engineering - Universita' degli Studi di Padova.

Awards and Fellowships

DARPA Young Faculty Award, Class of 2018.
ICME Poster Award, ICME Expo 2016, Stanford University - "Poster that best exhibits interdisciplinary research that connects mathematics, engineering, applied science, and computation in a way that represents the ICME mission".
American Heart Association Postdoctoral Fellowship, 2015-2016.


Research Experience for Undergraduate Students - University of Notre Dame

I'm looking for motivated undergraduate students determined to tackle research challenges in stochastic modeling, uncertainty quantification and related fields. Sophomore or Junior students preferred. Joint supervision with Prof. Bertanha in the Department of Economics is a possibility for students with Major/Minor in ACMS/Economics. You will significantly improve your Python skills, learn parallel computing and use high performance computing resources through the Center for Research Computing at the University of Notre Dame.

Research Interests

Uncertainty Analysis

Sparse Multi-resolution Expansion for Uncertainty Propagation - Development of techniques to efficiently propagate input uncertainties through multi-physics numerical models characterized by non-smooth stochastic response. Use of sparsity promoting greedy heuristics in the context of compressive sampling and its Bayesian counterpart, in an effort to minimize the number of deterministic simulations needed for an accurate statistical characterization of the output response.
Bayesian Parameter Estimation through adaptive MCMC sampling - Application of inverse Bayesian estimation and adaptive MCMC algorithms to determine the posterior distribution of input parameters characterizing 0D and coupled 3D-0D circulation models. Issues related to structural and practical identifiability of the parameters of these systems and use of sub-models to determine prior information are also of interest.

Stochastic multi-scale modeling and Clinical Applications

Stochastic multi-scale modeling of Cardiovascular Flow - Application of inverse and forward UQ to characterize the variability of hemodynamic indicators of interest induced by uncertainty in clinical data, model geometry, vessel wall material properties and other factors. Determination of the confidence associated to simulation outputs and measures of their sensitivity to a variation in the input quantities. Development of condensation approaches that significantly reduce the computational cost of porforming model tuning and optimization.
Finite Element Modeling of Cardiovascular Flow - Development of an integrated environment for creating parametric anatomical models from clinical image data and solve the associated hemodynamics through finite element analysis. This includes the revitalization of the SimVascular open-source framework, that provides a complete pipeline from medical image data segmentation to patient specific blood flow simulation and analysis.

Solenoidal Filtering of 3D MRV-PIV and Relative Pressure Estimation

Solenoidal Filters for 3D Velocitimetry Datasets - Development of matching pursuit heuristics to iteratively project a three-dimensional velocity field onto solenoidal vortex frames. Development of denoising algorithms that can be applied to both structured and unstructured measurement layouts with awareness to boundary conditions and solid walls.
Estimation of Relative Pressures - Development of techniques to estimate the relative pressures from three-dimensional velocimetry data, acquired through Magnetic Resonance Velocimetry (MRV) or Particle Image Velocimetry (PIV). Effect of velocity filtering on pressure estimation and models to estimate the relative pressure under turbulent flow conditions.