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You\u2019d be hard-pressed to find a field with bigger, richer, and more scientifically valuable data than particle physics. Years before \u201cdata scientist\u201d was even a term, particle physicists were inventing technologies like the world wide web and cloud computing grids to help them distribute and analyze the datasets required to make particle physics discoveries. Somewhat counterintuitively, though, deep learning has only really debuted in particle physics in the last few years, although it\u2019s making up for lost time with many exciting new advances.\n\nThis episode of Linear Digressions is a little different from most, as we\u2019ll be interviewing a guest, one of my (Katie\u2019s) friends from particle physics, Alex Radovic. Alex and his colleagues have been at the forefront of machine learning in physics over the last few years, and his perspective on the strengths and shortcomings of those two fields together is a fascinating one.