Short Bio

I received my PhD in Computational Mathematics at the University of Padova, where I also held a one year research fellowship year at the Department of Information Engineering. Before joining CERN, I was part the HPC/Cloud group at Leonardo Labs, where I was involved in the design and implementation of cloud stacks for Big Data analytics. Previously, I have worked on the massively parallel solution on GPUs of linear systems arising from differential equations such as high-fidelity numerical simulations of non-compressible unsteady Navier-Stokes equations and BVODE problems. In particular, I have explored the performance attained by the approximate iterative solution on GPUs of sparse triangular linear systems in the context of ILU preconditioning with application to non-miscible dual fluid flow simulations. Moreover, I developed a direct GPU solver tailored for structured matrices arising e.g. from the discretization of two point boundary value problems. I have also dealt with the efficient solution of ill-posed problems my means of low-rank models and statistical techniques with application to analysis of datasets in high dimensional spaces. I developed a block pivoting technique which can be adopted to increase performance in rank deficient QR computations and in sparse recovery problems.

For further information please check out my cv.

Teaching

MOOC: Scientific Computing in Python (in Italian)
Introduction to Python ( Italian version )

Software

LHDM - Active-set solver for NonNegative Least Squares based on Deviation Maximization column selection
QRDM - QR factorization for rank-deficient matrices based on Deviation Maximization column selection
dCATCH - Nearly-optimal polynomial regression designs on high-dimentional point clouds
PARASOF - Direct GPU solver for linear systems with BABD structure
ns2div - Hybrid CPU-GPU solver for Navier-Stokes equations with variable density

Publications

Preprints

Conference Papers

Journal Papers

PhD thesis