Organizers:
Miranda Cheng, Academia Sinica
Michael Douglas, Harvard University
James Halverson, Northeastern University
*Fabian Ruehle, Northeastern University
Progress in deep learning has traditionally involved experimental data, but in recent years it has impacted our understanding of formal structures arising in theoretical high energy physics and pure mathematics, via both theoretical and applied deep learning. This conference will bring together high energy theorists, mathematicians, and computer scientists across a broad variety of topics at the interface of these fields. Featured topics include the interface of neural network theory with quantum field theory, lattice field theory, conformal field theory, and the renormalization group; theoretical physics for AI, including diffusion models and equivariant models; ML for pure mathematics, including knot theory and special holonomy metrics, and deep learning for applications in string theory and holography.
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*organizer responsible for participant diversity
Winter Conferences
From December through April each year, the Aspen Center for Physics hosts between six and eight one-week winter conferences. These single-session meetings, with typical attendance of about 80, are focused on the latest developments in the core physics areas of the Center. The details of the format vary, but most have a set of invited speakers, additional speakers drawn from the conference participants, and poster sessions that give an opportunity for all participants to present and discuss their work.