I am a fixed-term researcher (RTDa) since January 2022 at the Department of Mathematics “Tullio Levi-Civita” of the University of Padova. I got my master degree in Mathematics and my PhD in Health Planning Sciences at the University of Padova in 2016 and 2021, respectively.
In my master thesis and during my PhD, I worked in the field of Magnetic Particle Imaging (MPI), which is a novel tracer-based medical imaging technique, studying effective approximation schemes suitable for reconstructing signals along Lissajous curves, which are typical sampling trajectories in a MPI scanner. Moreover, I studied and applied kernel-based machine learning techniques in the context of rare diseases diagnosis and in the classification of patient-derived xenografts.
After the PhD, I started collaborating with the MIDA group of the University of Genova in the field
of space weather forecasting, and I analysed and applied deep learning methods. Then, I won a one year postdoctoral grant sponsored by the Istituto Nazionale di Alta Matematica (INdAM). The research project linked to my current position concerns p-Laplacians on hypergraphs and related machine learning approaches.
My research interest is mainly in approximation theory, kernel-based approximation and machine learning, deep learning, medical imaging and space weather forecasting. I am part of the research groups CAA (Constructive Approximation and Applications) between the Universities of Padova and Verona, Rete Italiana di Approssimazione (RITA) and Methods for Image and Data Analysis (MIDA).