I’m a post-doctoral researcher focusing on numerical analysis and data science.
My research activity concentrates on developing reduced-order models for the efficient solution of uncertainty quantification and inverse problems. The numerical models, such as the ones arising from the discretization of partial differential equations, could be affected by a significant amount of uncertainties related e.g. to both physical and geometrical parameters. Developing uncertainty quantification and inverse techniques is thus crucial for a personalization of these models. My main interests concern the development of engineering applications, such as cardiac electrophysiology and mechanics.
In February 2017, I received my PhD in Mathematical Models and Methods in Engineering from Politecnico di Milano University, under the supervision of Prof. Alfio Quarteroni. My thesis, entitled Reduced-order models for inverse problems and uncertainty quantification in cardiac electrophysiology, regards the development of reduced-order models for the efficient and accurate solution of uncertainty quantification and inverse problems arising in cardiac electrophysiology. From 2017 to April 2018, I was a post-doctoral researcher at École polytechnique fédérale de Lausanne. I have joined the iHEART project as a post-doctoral researcher in May 2018.
Our preprint Statistical closure modeling for reduced-order models of stationary systems by the ROMES method is now available online (link to the code and further details in Projects).
I have participated to the FoMICS-DADSi Summer School on Data Assimilation in Lugano, Switzerland September 11–15, 2018.
I gave a talk entitled “Reduced-order modeling for Uncertainty quantification of the Cardiac function” at the 13th World Congress in Computational Mechanics in New York, New York July 23–27, 2018.
Our accepted paper Numerical approximation of parametrized problems in cardiac electrophysiology by a local reduced basis method is now available online (link to the 1D code and further details in Projects).
I gave a talk entitled Statistical Modeling of ROM State-space Errors by the ROMES Method at the SIAM Conference on Uncertainty Quantification in Orange County, California April 16–19, 2018.
I gave a talk entitled Reduced-order models for uncertainty quantification and parameter estimation in cardiac models at the ECCOMAS Young Investigators Conference in Milano, Italy September 13–15, 2017.
I gave a talk entitled Error Surrogates for Reduced-Order Models Based on Machine Learning Techniques at the SIAM Conference on computational science and engineering in Atlanta, Georgia February 27-March 3, 2017.
Get in touch
Drop me an e-mail at stefano.pagani at polimi.it