MIT Physics Professor Phiala Shanahan has been honored with the prestigious 2020 Kenneth G. Wilson Award for her groundbreaking work in lattice field theory, particularly for her innovative integration of artificial intelligence and machine learning techniques into this complex physics domain.
This internationally recognized award celebrates Shanahan's pioneering research applying advanced computational methods, including lattice Quantum Chromodynamics (QCD) enhanced with artificial intelligence algorithms, to unlock mysteries of hadrons and atomic nuclei. Her work represents a significant leap forward in how machine learning can revolutionize theoretical physics.
Shanahan's research focuses on understanding the fundamental structure of matter by exploring theoretical nuclear and particle physics through the lens of the Standard Model. Her innovative approach combines traditional physics methods with cutting-edge artificial intelligence to analyze quark and gluon interactions at the most fundamental level.
In her recent studies, Shanahan has utilized supercomputing resources combined with machine learning algorithms to reveal the crucial role of gluons—the force carriers of strong interactions—in hadron and nuclear structure. Her team achieved the first-ever calculation of the gluon structure of light nuclei, making predictions that will be tested in upcoming experiments at Jefferson National Accelerator Facility and the planned Electron-Ion Collider.
"To receive recognition from experts who deeply understand the technical nuances of my work is truly humbling," Shanahan remarked. "This achievement reflects not only my efforts but the incredible work of my talented students and postdocs, as well as my inspiring colleagues at the Center for Theoretical Physics who foster such a collaborative research environment."
The award presentation will take place during the November 12 virtual Bethe colloquium series, where Shanahan will be presented with a certificate recognizing her contributions, a monetary prize, and the opportunity to showcase her AI-enhanced research methodology.
Shanahan's first-principles approach to understanding matter structure aims to advance nuclear physics experiments seeking to constrain physics beyond the Standard Model, including dark matter investigations. "Our research into nuclear structure and reactions is at the threshold of a new precision era in understanding how nuclei emerge from fundamental particle physics," she explained. "Bridging this gap represents an incredibly exciting frontier in science."
Regarding her machine learning research, Shanahan noted, "We're revolutionizing how numerical lattice field theory calculations are performed by developing new AI-powered algorithms that enable previously impossible computations. When Aurora, the world's most powerful supercomputer, becomes operational in the coming years, we'll be positioned to leverage its capabilities in entirely novel ways through our artificial intelligence frameworks."
After earning her BS in 2012 and PhD in 2015 from the University of Adelaide in Australia, Shanahan joined MIT as a postdoctoral researcher. She later held a joint appointment as assistant professor at the College of William & Mary and senior staff scientist at Thomas Jefferson National Accelerator Facility before returning to MIT in 2018. Among her numerous accolades are a National Science Foundation CAREER award, a U.S. Department of Energy Early Career Award, an Emmy Noether fellowship (2018), recognition in Forbes Magazine's 30 under 30 in Science (2017), and inclusion in Science News' 10 Scientists to Watch (2020).
Established in 2011, the annual Kenneth G. Wilson Award for Excellence in Lattice Field Theory honors physicists who have made recent outstanding contributions to the field. Named after Nobel laureate Kenneth Wilson (1936–2013), who founded lattice gauge theory in 1974, the award recognizes innovations that enable numerical study of complex physical theories using powerful computing technologies enhanced by artificial intelligence and machine learning approaches.
