Phiala Elisabeth Shanahan is an Australian theoretical physicist who lives and works in the United States. She is known for her work on the structure and interactions of hadrons and nuclei and her innovative use of machine learning techniques in lattice quantum field theory calculations.[1]
After completing her PhD, Shanahan became a postdoctoral associate at the Massachusetts Institute of Technology from 2015 to 2017.[3][6] During this time, she studied the role of force-carrying elementary particles called gluons in the structure of subatomic particles called hadrons. She also used lattice quantum chromodynamics techniques to examine the structures of atomic nuclei.[5] In 2017, Forbes featured Shanahan in its "30 Under 30: Science" list for the impact of her work on the understanding of dark matter and physics beyond the Standard Model.[9] From 2017 to 2018, she held a joint appointment as assistant professor at the College of William & Mary and senior staff scientist at the Thomas Jefferson National Accelerator Facility.[6][1] Shanahan became assistant professor in the Center for Theoretical Physics at the Massachusetts Institute of Technology in July 2018,[1][6] which at that time made her the youngest assistant professor of physics there.[10] Shanahan was also a Simons Emmy Noether Fellow at the Perimeter Institute for Theoretical Physics during the fall 2018 semester.[1][10] This fellowship supports early- and mid-career women physicists.[11] Shanahan's current research includes seeking to understand how the structures and interactions of hadrons and atomic nuclei can be calculated from the fundamental principles of the Standard Model of physics, the role of gluons in the structures of hadrons and atomic nuclei, and how supercomputers and machine learning may be used to perform low-energy quantum chromodynamics calculations. Some of the predictions she is currently developing may be testable in the future using the Thomas Jefferson National Accelerator Facility's planned electron-ion collider.[10][6]
Shanahan received the American Physical Society's 2021 Maria Goeppert Mayer Award, which recognizes outstanding achievement by early-career women physicists, for her "key insights into the structure and interactions of hadrons and nuclei using numerical and analytical methods and pioneering the use of machine learning techniques in lattice quantum field theory calculations in particle and nuclear physics."[1][12]
This page is based on this Wikipedia article Text is available under the CC BY-SA 4.0 license; additional terms may apply. Images, videos and audio are available under their respective licenses.
