Ben Nachman, a staff scientist in the Physics Division, has been elected to a Chair-line role in the American Physical Society’s (APS) Topical Group on Data Science.

As head of the Physics Division’s Machine Learning Group, Nachman leads a cross-cutting effort that connects researchers who are developing, adapting, and deploying artificial intelligence (AI) and machine learning (ML) solutions to fundamental physics challenges across all high-energy physics frontiers including theory. Data science, which already has innumerable applications across all STEM fields, is a highly interdisciplinary and quickly growing field at the intersection of statistics, computer science, and mathematics. According to Nachman, “We have some of the largest and most complex scientific datasets in existence, which require state-of-the-art data science tools to analyze. At the same time, physics has unique challenges and opportunities that require dedicated data science tools. This has created Data Physicists, a new type of researcher working at the intersection of theory and experiment. The APS Group on Data Science is a home for all of these researchers, as well as the growing community of physicists who are applying data science tools to a wide array of problems in other areas of science and in industry.”

Initially elected as Vice Chair for the 2024-2025 academic year, Nachman’s role will proceed in successive years to Chair-Elect, Chair, and then Past Chair. Ben joins two other Physical Sciences Area scientists who also serve in APS leadership roles. ATAP senior scientist and Division Director Cameron Geddes is currently serving as Chair-Elect of the APS Division of Plasma Physics, and Soren Prestemon, ATAP Deputy Division Director for Technology and a senior scientist in Berkeley Lab’s Engineering Division, is the Chair-Elect of the APS Division of Physics of Beams.