My research interests are in particle physics, machine learning, statistics, open science, cyberinfrastructure, and science communication to the broader public.
I am primarily an experimental particle physicist working on the ATLAS experiment at the LHC, though my interests are quite broad. Early in my education I had a difficult time choosing between theoretical and experimental physics, and I decided to focus on the interface with a heavy emphasis on analysis techniques, statistical methodology, and cyberinfrastructure. I try to keep abreast of current thinking in theoretical physics to ensure that the results from the large experiments are relevant and impactful. At the same time, I like to keep the experimental program cleanly factorized from theoretical bias. Thus a theme of much of my work has been to establish the analysis strategy, statistical techniques, and cyberinfrastrure so that the experiments can provide powerful, interpretable tests for a wide range of theories.
Recently, I've been particularly interested in inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, use of adversarial training for robustness to systematic uncertainty, the use of generative models in the physical sciences, and integration of reproducible workflows in the inference pipeline.