Source: symmetry (joint Fermilab / SLAC publication)

Authors: Laura Dattaro

Published Date: 2024-05-07T09:20:19-0500

Summary: Radha Mastandrea, initially skeptical of machine learning during her undergraduate years at MIT, now uses it daily as a doctoral student at UC Berkeley to hunt for new physics in data from the Large Hadron Collider. This shift mirrors a broader trend in physics over the past decade, as machine learning has become crucial for analyzing complex data. Kazuhiro Terao from SLAC emphasizes the need for improved machine learning education in physics to prepare students for these challenges. Experience in machine learning opens opportunities beyond academia, as shown by physicists like Kylie Ying and Lucas Borgna, who transitioned to industry roles. Structured training programs and workshops, such as SLAC’s Summer School and the American Physical Society’s initiatives, are beginning to address this educational gap. The field is evolving, with physicists gaining valuable skills applicable across various industries.

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