I am a Ph.D. candidate in physics at the University of Texas at Austin, studying theoretical cosmology.
My research interests are in primordial cosmology, the physics of the dark sector as well as the use of cosmological probes to constrain new physics in the early and late universe.
I am a determined individual, always willing to learn and explore new research topics.
See my CV below for a full list of my papers, research presentations and visits, as well as my teaching experience, outreach activities and more.
To see a full list of my papers and most recent publications, please visit my InspireHep page at this link.
Advised by Katherine Freese
1-semester student exchange program
My research combines theory and data-driven approaches to tackle fundamental questions in cosmology, like the nature of the dark sector and inflation. My work focuses both on model-building as well utilizing precise Cosmic Microwave Background measurements to explore new physics in the early and late universe.
Whenever I don't do physics, I run. I am specialized in the 400m hurdles and I am currently training to represent Italy at the next Olympics games in Paris in the Summer 2024.
In my remaining (limited) free time, I enjoy cooking typical Italian dishes, spending time outdoors, hiking and riding my motorcycle.
My work as a researcher centered on the analysis and classification of the latest cluster catalog from the Atacama Cosmology Telescope (ACT) collaboration, with the goal of detecting the lensing induced cluster signature through a CMB gradient oriented stacking.
As a teaching assistant, I supported the labs and homework administration and grading for the physics introductory course in mechanics (Fall) and electromagnetism (Spring).
My work centered on the development of a machine learning (ML) algorithm using a partial convolutional neural network (PCNNs) to in-paint masked images of the cosmic microwave background (CMB). The network we developed can reconstruct both the maps and the power spectra to a few percent for circular and irregularly shaped masks covering up to ~10% of the image area.
Finally, our model also outperforms the other recently developed ML methods that inpaint pure CMB maps. For more info click on the link below:
Inpainting CMB maps using Partial Convolutional Neural NetworksI worked as a research assistant in the photo-electronics group for the DarkSide-20k experiment, a 20-ton liquid argon detector to search for WIMP dark matter particles held at LNGS. Specifically, I was in charge of testing and analyzing different Silicon PhotoMultipliers (SiPMs) variants and their integrated electronics.
I consulted over 30 students from a variety of disciplines to develop an individualized and strategic approach to learning that enables the student to make the most of lectures, precepts, and readings; to manage their time; and to achieve their goals while maintaining a healthy balance.
My work spanned from developing effective-reading and study strategies to time management and semester/course planning.