Research

1. Physics questions
My research focuses on how we can design, operate, and analyse particle-physics experiments to learn as much as possible about the fundamental laws of nature. The top quark and the Higgs boson are central to this work: their interactions provide sensitive tests of the Standard Model and may reveal subtle signs of new physics. I contribute to measurements with the CMS experiment, particularly involving rare processes such as the simultaneous production of four top quarks and the production of a Higgs boson together with top quarks.

2. Designing better experiments
A detector should be designed around the scientific questions it is intended to answer. I develop methods that use machine learning, differentiable simulation, and numerical optimisation to study detector designs as complete systems. Instead of optimising individual components independently, these approaches connect detector geometry, reconstruction algorithms, and physics performance.

3. Reconstructing particles with machine learning
Particle detectors do not observe particles directly: they record many individual signals that must be combined into a physical interpretation of an event. At future experiments and the High-Luminosity LHC, this reconstruction problem will become considerably more complex. My work explores graph neural networks and other machine-learning methods for scalable, robust particle reconstruction in highly granular detectors.

4. From algorithms to scientific results
New methods are useful only if they can operate reliably on real experimental data and at the required scale. I therefore also work on GPU computing, inference as a service, and the integration of machine-learning models into scientific workflows. The aim is to connect new algorithms with real measurements—and ultimately improve what an experiment can discover. These areas offer student projects ranging from physics analysis and scientific computing to machine learning, simulation, and detector optimisation.