I am a postdoctoral fellow in the professor Karen Wilcox'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's 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 modelling, uncertainty quantification, and real-world applications with economic and societal impacts.
In the last years I focused on the development 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. This resulted in the creation of several scientific Python packages. We also coupled ROMs with reduction in parameter space using active subspaces (AS). We studied the effect of incorporating low-intrisic dimensionality bias in a multi-fidelity setting to enhance regression and solution manifold reconstruction. In the last few years we also extended AS developing kernel-based active subspaces and local active subspaces.
I received my Ph.D. in Mathematical Analysis, Modelling, and Applications at International School of Advanced Studies (SISSA), Trieste, Italy, where I was part of the SISSA mathLab group
, under the supervision of professor 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.