I am interested in methods that let us infer knowledge from data. The main focus of my research is simulation-based (or likelihood-free) inference: statistical methods for the case when we can model a phenomenon with a computer simulation, but we cannot calculate its likelihood function. This scenario is very common in scienctific fields from neuroscience to epidemiology and from elementary particle physics to cosmology. We have pioneered new inference techniques that combine some understanding of the latent processes in the simulator, a dash of classical statistics, and a heavy dose of machine learning.
Applied to problems 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 measurement problem at the LHC experiments. I also worked on forecasting methods based on information geometry for particle physics.
But these methods are not limited to particle physics. We used the same techniques to learn about dark matter through gravitational lensing. I am excited to find out how they can help us understand what is happening in problems in many other scientific domains.
Beyond scientific use cases, I am generally interested in (approximate) inference, normalizing flows, and generative models. I have led the devlopment of manifold-learning flows, a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.