About
My academic research is at the interface of computational and theoretical tools, applied to solving the mysteries of the Universe. Current projects span from using deep learning and quantum simulations to understand quantum gravity, to high-performance-computations of subatomic particles in theories that could explain the origin of matter.
I am working on projects related to low-energy nuclear physics, for example calculating nucleon-nucleon interactions or nuclear form-factors directly from the theory of Quantum Chromo-Dynamics (QCD). In addition, I study matrix models in the context of the gauge/gravity duality conjecture with the aim of understanding the possible intriguing relation between gauge theories and quantum gravity. In these projects, I apply Machine Learning (ML) approaches to a large amount of numerical data available from Monte Carlo simulations with the goal to make new and unexpected discoveries. Recently, I also used quantum computing simulations to enable further discoveries on these matrix models.
My expertise is in Markov Chain Monte Carlo (MCMC) numerical simulations of quantum field theories, also known as Lattice Field Theory simulations, which use massively parallel supercomputers (CPU and GPU-based) around the world to solve the complex equations hiding the mysteries of particle physics. MCMC is also used in the field of parameter inference to compute the posterior probabilities of parameters involved in fitting a specific theoretical model to observational or simulated data.
