To achieve some of the biggest physics discoveries in the last decade, including the Higgs boson, gravitational waves, and black holes, physicists had to radically re-imagine the paradigm of working in small teams and instead construct large-scale experimental collaborations of hundreds or even thousands of scientists. The recent success of large-scale AI "foundation models" in various domains begs the question: could our scientific conventions yet again be restricting our access to major discoveries? In this talk, I propose that a multi-disciplinary approach to fundamental physics research will be critical to finally answering the grand scientific mysteries about our Universe that have thus far eluded our usual strategies. To achieve this vision, AI methods can help us publish detector-agnostic datasets, construct richer embeddings of our data, and highlight connections across varied domains -- but we also need to take care to ensure that we design these tools to uphold our highest priorities as scientists.

Shot Bio: Dr. Mariel Pettee is an interdisciplinary scientist based in Brooklyn, NY. She is a Chamberlain Postdoctoral Research Fellow at Lawrence Berkeley National Laboratory, a visiting researcher at the Flatiron Institute Center for Computational Astrophysics in New York City, and a member of the ATLAS Experiment at CERN. Her scientific research is centered on developing new AI methods to help make discoveries in high-energy particle physics and astrophysics. As a founding member of the Polymathic AI collaboration, she is interested in harnessing multidisciplinary AI foundation models for scientific insight. She received a PhD in Physics from Yale University, a Masters in Physics at the University of Cambridge, and a Bachelors in Physics & Mathematics from Harvard University.