Machine Learning and Benchtop Experiments

Machine learning has generated much recent excitement within the physics community and provides a powerful new tool to analyze and understand many physical systems. However, in the experimental study of complex physical systems, the usage of machine learning is still in its infancy. Specifically, it is not obvious which scientific questions are susceptible to machine learning disruption and, even more interesting, which questions are not? In this talk, I will address our approach to these questions, sharing our attempts to leverage lab models of complex systems for this study. I will discuss our tactics to holistically amalgamate experiments with simulations.