Abstract: In recent years, enormous data sets have begun to appear in real-space (scanning probe) and reciprocal-space (scattering probes visualizations of electronic quantum matter. The increasing volume and variety of this data present new challenges and opportunities that are ripe for a new approach: machine learning. However, scientific questions in the field of electronic quantum matter require fundamentally new approaches to data science for two reasons: (1) quantum mechanical imaging of electronic behavior is probabilistic and (2) inference from data should be subject to fundamental laws governing microscopic interactions.

In this talk, I will review the aspects of machine learning that are appealing for dealing with quantum complexity and present how we implemented a machine learning approach to analysis of scanning tunneling spectroscopy data.