The Theoretical Advanced Study Institute in Elementary Particle Physics (TASI) recently concluded its 40th year, bringing 65 students from around the world to Boulder to delve into the “Frontiers of Particle Theory.” 

Supported by the National Science Foundation and the University of Colorado Boulder, TASI brings graduate students to Boulder every summer to explore an advanced topic in theoretical particle physics presented by distinguished lecturers. Professors Oliver DeWolfe and Ethan Neil serve as local organizers and Co-Directors of TASI. 

“As in previous years, we were delighted to host a large group of talented particle theory graduate students here in Boulder for this year’s TASI summer school,” said DeWolfe and Neil.  

They added, “TASI was a great success, with four weeks of intense interactions among the students and expert lecturers from around the world who shared their knowledge on a wide variety of particle physics topics, including an emphasis on the use of cutting-edge machine learning and artificial intelligence methods.” 

As part of the summer program’s outreach efforts, Dr. Ben Nachmann from Lawrence Berkeley National Laboratory presented a public lecture on the use of AI to study fundamental interactions in particle physics. 

DeWolfe and Neil expressed praise for the scientific organizers behind the school’s focus each year. “A rotating set of external scientific organizers ensures that the school’s focus is always topical and interesting.” This year’s external organizers were Professors Nathaniel Craig (UC Santa Barbara), Tongyan Lin (UC San Diego) and Jesse Thaler (MIT). 

Visit the website to learn more about this year’s program and view lectures. Applications for TASI 2025 open in January and will be due March 1, 2025. 

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Presented by: Dr. Benjamin Nachman, Lawrence Berkeley National Laboratory

Monday, June 17th, 2024
7:30 p.m. – 9:00 p.m.

Duane Physics and Astrophysics Building, room G1B20

2000 Colorado Ave,
Boulder, CO 80309

Price: free

Abstract: Particle, nuclear, and astrophysics experiments are producing massive amounts of data to answer fundamental questions about the basic constituents of our universe. While researchers in these areas have been using advanced data science tools for decades, modern machine learning has introduced a paradigm shift whereby data can be directly analyzed holistically without first compressing it into a more manageable and human understandable format. How will the machines help us explore the unknown? Can they be trusted to give us the right answers? I’ll attempt to address these questions and others with a talk about the use of modern machine learning, including generative AI, in the study of fundamental interactions.

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