I am broadly interested in methods that let us infer scientific knowledge from data. I have been focusing on likelihood-free inference: these statistical methods allow us to measure model parameters when we can model phenomena with complex computer simulations, but these do not have a tractabe likelihood function. New techniques combine some understanding of the latent processes in the simulator, a dash of classical statistics, and a heavy dose of machine learning.
In particle physics, these methods allow us to measure fundamental physics parameters with a higher precision than traditional methods, and might ultimately help us understand the universe a little bit more. More generally, I am interested in new statistical methods and machine learning for the LHC experiments, including new techniques based on informatin geometry, effective field theories, and the phenomenology of the Higgs boson at the LHC.
But these new inference methods are not limited to particle physics, and I am excited to find out how they can help us understand what is happening in fields ranging from cosmology to economics to epidemiology.