REU Site: Artificial Intelligence in Modern Physics Research

The Physics Department at Washington University in St. Louis (WashU) addresses a fundamental challenge in modern physics education: preparing the next generation of scientists to combine traditional physics understanding with Artificial Intelligence (AI) methods. Through this Research Experiences for Undergraduates (REU) site, WashU provides a fully immersive 10-week experience for undergraduate students to perform cutting-edge research in fundamental physics. The participants, selected nationwide with particular focus on students in the Midwestern region, receive stipends, housing, and travel funds to ensure broad accessibility. The program begins with an AI boot camp providing hands-on machine learning training, after which participants join research groups and apply what they learn to analyze large datasets and address realistic physics problems. The REU program benefits society by preparing participants for multiple career paths, developing technical skills that are highly transferable across physics subfields and industries beyond academia. This REU Site addresses fundamental challenges in modern physics through the innovative application of AI across multiple subfields including astrophysics, nuclear physics, atomic, molecular, and optical physics, condensed matter physics, and biophysics. The program advances the field by unifying physics research through the lens of AI, focusing on three transformative machine learning applications: image segmentation & recognition, time-series or spectral analysis, and reduced models & feature identification. These computational methods tackle crucial research questions ranging from identifying emergent properties in 2D materials and classifying meteorite characteristics to determining exoplanet atmospheric composition, analyzing X-ray emission from black hole binaries, uncovering features of nucleon-nucleon interactions, and reconstructing material magnetization maps. The program's methodological framework represents a significant advancement in physics research, developing machine learning tools that transcend specific subfields and establish a versatile approach applicable to physics and other scientific disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.