Shameer Ahmad

PhD thesis: TBC

My PhD project focuses on advancing searches for Beyond Standard Model (BSM) physics, with a particular emphasis on R-Parity Violating Supersymmetry and dark matter signatures in the ATLAS detector. By integrating a principled understanding of machine learning, I aim to design algorithms grounded in detector physics and event kinematics. This approach seeks to enhance event reconstruction resolution, improving the identification and separation of subtle BSM signals from Standard Model backgrounds. The project combines theoretical insight into BSM scenarios with practical expertise in data acquisition and detector response, ensuring that ML models are interpretable, physically motivated, and robust against systematic uncertainties. My the work aspires to expand ATLAS’s discovery potential, enabling more sensitive probes of new physics in previously inaccessible regions of parameter space.

Who or what inspired you to pursue your research interests?

I was inspired during my Master’s project, which focused on a statistical and data-driven approach to di-Higgs discovery. I enjoyed the intellectual challenge of extracting faint physics signals from complex data, the blend of theory and computation, and the sense of contributing to a larger scientific effort. This experience revealed how advanced data analysis can directly expand our understanding of fundamental physics. My supervisor’s encouragement and enthusiasm were pivotal, providing guidance, fostering curiosity, and showing me the excitement of pushing experimental boundaries.