As experimental particle physics, my research has focused on measurements of Standard Model processes, first, and subsequently on searches for new physics at hadron colliders. These include searches for Supersymmetry (third generation bottom and top squarks, electroweakinos), WIMP-like dark matter candidates (SUSY, extended Higgs sectors), leptoquarks, and hidden sectors leading to long-lived particles (dark photons, axion-like particles). Member of CDF, FNAL (2000-2011), of ATLAS, CERN (2001-Present), and FASER, CERN (2020-Present), I led or directly contributed to more than forty publications in the past decade, and I have been appointed for several national and international roles including Supersymmetry and Physics Modelling ATLAS working groups convener (2012-2017); ATLAS UK PI (deputy until 01/26, 15 institutes); FASER UK PI (4 institutes); team leader of the Liverpool ATLAS and FASER groups. I collaborate with experimentalists and theorists for future facilities’ plannings and medium and long-term high energy physics projects: the upgrade of the ATLAS detector at the High Luminosity LHC (HL-LHC, 2028-2040); a proposed electron-proton facility at CERN (LHeC) to be built in parallel to the HL-LHC, for which I also co-coordinate the general-purpose high-energy physics programme; the proposed Forward Physics Facility (FPF) at CERN, meant to host a suite of experiments targeting long-lived particles and high-energy neutrinos; and the Future Circular Collider (FCC), the ultimate 100-km accelerator proposed to be built after 2040 at CERN. For my research, I exploit modern data-analysis tools based on AI/ML approaches. Further to this, I work on projects that utilise such tools in the broader context. I co-initiated an international consortium with computer and medical scientists for the development of explainable AI tools to be applied also in medical physics and neuroscience (Multi-disciplinary Use Cases for Convergent new Approaches to AI explainability, MUCCA), and collaborate with industrial partners also through supervision of students co-funded by the LIV.INNO Centre for Doctoral Training.