I work on Machine Learning and Approximation Theory, and on their combination for the solution of problems in simulation and social sciences. Recently, I’m gaining an increasing interest in their application to network data.
I’m also an Associated Member in the Cluster of Excellence Data-integrated Simulation Science, where I’m a member of the project network Machine Learning for Simulation.
Previously, I’ve been for four years in the group of Bernard Haasdonk at the University of Stuttgart, working on kernel based algorithms and their application to the surrogate modelling of complex simulations.
I obtained my MSc and PhD at the Department of Mathematics of the University of Padova under the supervision of Stefano De Marchi in the CAA group, where I wrote my theses on algorithmic and theoretical aspects of kernel based interpolation.
You can find more info in my Curriculum.