I am a PhD candidate in Mathematical Physics in the research group of Prof Marcos Mariño at the University of Geneva, Switzerland, funded by the ERC Synergy Grant ReNewQuantum, and affiliated with the National Centre of Competence in Research SwissMAP.

Since September 2020, I am a member of the Scientific Council of the international conference centre SwissMAP Research Station in Les Diablerets, Switzerland.

Here are my CV and a list of attended events.

PhD in Mathematical Physics

University of Geneva (in progress)

MSc in Mathematical and Theoretical Physics

University of Oxford (2019)

BSc in Physics

Sapienza University of Rome (2018)

Contributed to the design of an experimental framework for the detection of muon-philic light Dark Sector particles in proton beam dump experiments. Implemented a model to exploit the displaced-vertex signal from the secondary muons of NA62-type experiments to probe the parameter space of a theoretically conjectured light exotic scalar generated via muon bremsstrahlung. Produced a sensitivity projection using programming language C++, data analysis software ROOT, and simulation software MadGraph5_aMC@NLO. NA62 is a proton-on-target collision experiment recently searching for Beyond Standard Model physics at low energies at the Super Proton Synchrotron.

Collaborated with Prof Francis Brown (University of Oxford) on the application of motivic Galois theory to the study of Feynman integrals in perturbative QFT, specifically investigating the motivic Galois coaction and factorisation theorems for scalar Feynman graphs with non-generic kinematics. Collaborated with Prof Francesco Riva (University of Geneva) on EFT contraints arising from fundamental assumptions of UV consistency, specifically investigating the restrictions placed by (beyond-)positivity bounds on (beyond-)Horndeski theories of modified gravity. Attended courses on Random Matrix Theory.

Contributed to a Deep Learning predictive model for preventative maintenance of large infrastructures equipped with alarm nets. Project implemented using Bayesian Neural Networks and programming language R and customized to fit the specific needs of the commissioning telecom company. Pangea Formazione is a Big Data Analytics and AI company providing customised software for management consulting and training.

Contributed to the development of a Monte Carlo optical simulation of the Small-Angle Calorimeter of PADME’s detector using simulation software Geant4 and programming language C++. Characterised the performance of a single PbF2 crystal attached to a Hamamatsu R13478UV photomultiplier tube with focus on time and energy resolutions using data analysis software ROOT. PADME (Positron Annihilation into Dark Matter Experiment) is a positron-on-target collision experiment searching for dark photon production at high intensity at the DAFNE Beam Test Facility.

In this talk I will discuss how underlying algebro-geometric structures characterise Feynman amplitudes as periods of motives and how …

Recently developed approaches to scattering amplitudes in quantum field theory highlight underlying geometrical structures which allow …

A Monte Carlo simulation of a prototype of PADME’s Small-Angle Calorimeter has been implemented on Geant4. Particular attention …

This article gives a short step-by-step introduction to the representation of parametric Feynman integrals in scalar perturbative …

The PADME experiment at Laboratori Nazionali di Frascati (LNF), Italy, will search for invisible decays of the hypothetical dark photon …