I am a postdoctoral fellow in
Karen Willcox's group at the Oden Institute, University of Texas at Austin.
I work within the fields of applied mathematics, scientific machine learning, and digital twins. My research interests include the development of data-driven reduced order methods and parameter space reduction techniques. Currently, I am working on a NASA University Leadership Initiative project, developing a predictive digital twin of an autonomous drone used for cargo missions in an urban environment. This integrates reduced order modeling, uncertainty quantification, and real-world applications with economic and societal impacts.
In the last years, I developed of non-intrusive reduced order methods such as proper orthogonal decomposition with interpolation and dynamic mode decomposition, with applications in naval, nautical, biomedical, and automotive engineering. I created several scientific Python packages. I also coupled ROMs with reduction in parameter space using active subspaces (AS). I studied the effect of incorporating low-intrinsic dimensionality bias in a multi-fidelity setting to enhance regression and solution manifold reconstruction. I also extended AS developing kernel-based active subspaces and local active subspaces.
I received my Ph.D. in Mathematical Analysis, Modelling, and Applications at the International School of Advanced Studies (SISSA), Trieste, Italy, where I was part of the
SISSA mathLab group, under the supervision of Gianluigi Rozza.
My industrial Ph.D. grant was sponsored by Fincantieri S.p.A. (see the project
here). The topic was the structural optimization of a passenger ship during the design step through parametric techniques and computational reduction.