Joint TUNL/HEP Seminar: Machine Learning in Nuclear Physics Research

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Speaker(s): Michelle Kuchera
Machine learning has become ubiquitous in data-rich applications. Fundamental physics research
provides an exciting realm for machine learning research with applications ranging from
experimental data acquisition through making theoretical predictions. This talk will step through
machine learning theory from neural networks to Generative Adversarial Networks, highlighting
applications in low through high energy nuclear physics research. Specifically, I will focus on
applications that our group, ALPhA (Algorithms for Learning in Physics Applications), have
completed in collaboration with the Facility for Rare Isotope Beams and the Thomas Jefferson
National Accelerator Facility. I will highlight areas where nuclear physics applications can
inform machine learning innovation, providing a rich environment for the advancement of both
nuclear physics and machine learning research.
Sponsor

TUNL Seminar Series

Co-Sponsor(s)

Physics