I am interested in methods that let us infer scientific knowledge from data. My main focus is simulation-based (or likelihood-free) inference: statistical methods for the common case when we can model a phenomenon with a computer simulation, but we cannot calculate its likelihood function. We have pioneered new techniques that 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 properties more precisely than before. I am the lead developer of the MadMiner library, which makes it straightforward to apply these algorithms to almost any LHC problem. I also worked on forecasting methods based on information geometry for particle physics and studied the phenomenology of effective field theories, a language that lets us characterize for instance the properties of the Higgs boson.
We generalized some of the methods that we originally developed for particle physics and applied them to a very different problem: to learn about dark matter properties from observations of strong gravitational lensing. In fact, these new ideas are not at all limited to physics, and I am excited to find out how they can help us understand what is happening in scientific fields ranging from economics to epidemiology.
Beyond scientific applications, I am broadly interested in (approximate) inference, normalizing flows, and generative models.