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FRIB Theory Alliance Hosts Summer School on Machine Learning

Artist's rendering of FRIB building

FRIB hosted the 2019 FRIB Theory Alliance (FRIB-TA) Summer School on machine learning in physics applications.

The school, held from May 20-23, was titled “Machine learning applied to nuclear physics.” It brought together graduate students, postdoctoral researchers, and senior scientific experts. They work in nuclear physics, mathematics, computer science, and related areas. They came together to discuss an important emerging science, how it applies to nuclear science, and reviewed publications on machine learning.

Hosted by scientists from universities and national laboratories, the summer school had nearly one hundred attendees. Students came from all over the United States, as well as Canada, the United Kingdom, Germany, and Norway.

Students listen to a lecture during the FRIB Theory Alliance Summer School on Machine Learning on 23 May 2019.

The FRIB-TA board solicits ideas each year for the summer school. They consider how the topics can connect to FRIB, as well as benefit the junior FRIB-TA membership. ”Once a theme is selected, we give the organizers the support and flexibility they need, and so far the demand always exceeds our expectations,” said Filomena Nunes, FRIB-TA managing director.

Machine learning allows computers to collect and analyze the data much faster than by hand. Machine learning is based on the idea that computers can learn from data and identify patterns on their own. Students heard from expert researchers on different kinds of machine learning. Students attended lectures and viewed examples to see how machine learning can be applied to nuclear physics.

“Machine learning is one of the largest growing fields in computer science today; many universities are setting up their own programs in the field,” said Morton Hjorth-Jensen, professor of physics and astronomy, who is an organizer at the summer school. “Not only will it revolutionize the amount of data we can process in our laboratories, machine learning has important applications in medicine and technology, too.”

From left: Morten Hjorth-Jensen, Raghuram Ramanujan, and Michelle Kuchera pose for a photo during a break of the 2019 FRIB-Theory Alliance Summer School on machine learning.

The FRIB Theory Alliance (FRIB-TA) is a coalition of scientists from universities and national laboratories who seek to foster advancements in theory related to diverse areas of FRIB science; optimize the coupling between theory and experiment; and stimulate the field by creating permanent theory positions across the country, attracting young talent through the national FRIB Theory Fellow Program, fostering interdisciplinary collaborations, and shepherding international initiatives. Learn more at the FRIB-TA website.

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