I am interested in methods that let us infer knowledge from data. A 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 may sound like an obscure special case, but is actually extremely common in science, with examples from neuroscience to epidemiology and from elementary particle physics to cosmology! New algorithms based on machine learning, active learning, and the tight integration of simulation and inference are changing what we can do in this field very rapidly, read our opinionated review. We also introduced new inference methods 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 automates these algorithms and 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 probabilistic and generative models such as normalizing flows, (approximate) inference, and the quantification of uncertainty. I have led the development 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.