Portfolio
This project aims to develop a reliable data-driven model of electron–nucleus scattering cross sections and to show that deep neural networks can learn meaningful features of nuclear physics.
This project aims to develop a generative model for simulating neutrino and antineutrino scattering on atomic nuclei. Using generative adversarial networks and transfer learning, we showed that neural-network models can reproduce key kinematic distributions, learn reusable information about lepton–nucleus dynamics, and efficiently adapt to new targets and interaction channels. The project opens a path toward next-generation fast event generators for neutrino physics.
Our project focused on investigating the information content of polarization asymmetries in neutrino–nucleon interactions. The work showed that spin-dependent observables provide sensitive probes of the nucleon axial structure and of the interplay between resonant and non-resonant mechanisms in pion production, in both charged-current and neutral-current processes.
Our project focused on applying deep learning methods to predict key transport properties of porous media directly from their geometry. The work showed that neural-network models can accurately infer transport coefficients and reconstruct concentration fields, providing efficient surrogate models for complex transport phenomena in structured materials.
Our project focused on identifying superalgebra structures that describe intrinsic symmetry. Starting from the Poincaré and Anti-de Sitter frameworks, we extended these structures using resonant construction and analyzed millions of candidates to identify viable superalgebras satisfying the Jacobi identities. The work resulted in a broad classification of new superalgebras and in explicit supergravity realizations for selected cases.
This project focused on applying deep learning and statistical uncertainty estimation to models for counting and classifying bacterial colonies in Petri dishes. The work led to improved U-Net and U2-Net-based solutions that increased counting accuracy and reliability in biomedical image analysis, even for images containing many diverse objects.
Our project focused on developing Bayesian neural network methods for studying the nucleon’s electroweak structure. The work led to new parametrizations of electromagnetic and axial form factors, studies of two-photon exchange effects, comparisons with quantum field-theoretical calculations, and a Bayesian determination of the proton charge radius.
Our project focused on improving the theoretical description of single pion production induced by neutrino–nucleon scattering, an important process in measurements of neutrino oscillation parameters. The work led to new models of resonance form factors, improved treatment of lepton-mass effects, and new parametrizations of the weak nucleon–Delta transition matrix element.
This project focused on radiative corrections generated by two-boson exchange in lepton–nucleon scattering. In elastic electron–proton scattering, we studied the role of two-photon exchange and compared neural-network and hadronic-model predictions for its impact on observables and extracted proton structure parameters. In charged-current neutrino reactions, we evaluated electroweak box corrections and showed that they can induce percent-level effects relevant for precision neutrino measurements.
This project explored whether Bloom–Gilman duality appears in neutrino–nucleon and neutrino–nucleus scattering and whether it can be used to model nucleon structure functions in the resonance region. The work showed that duality is not universal: within the Rein–Sehgal framework it is visible for proton targets in a limited kinematic range, but is much weaker or absent for neutron and isoscalar targets. These results clarified when resonance-region models can be consistently connected to deep-inelastic structure functions in neutrino cross-section calculations.
This project focused on extracting the electromagnetic form factors of the proton and neutron and on studying the two-photon-exchange contribution in elastic electron–proton scattering. The work provided a global fit to nucleon form-factor data with quantified uncertainties and used it to make statistical predictions for observables sensitive to radiative corrections beyond the Born approximation.
