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.


For my papers, see my INSPIRE profile or Google Scholar.

Most of my scientific code is published on GitHub. Perhaps the most interesting project there is the MadMiner library, a Python module that automates new inference techniques for particle physics measurements.


Find me in room 607 at the Center for Data Science or room 840 in the Department of Physics. Or write me at

What else?

I sometimes travel and take pictures: