Talks

Generative AI — a class of algorithms that learn complex probability distributions from data — is opening new avenues for discovery across fundamental physics. In this talk, I will highlight several recent applications. At the LHC, we are ushering in a new paradigm of model-agnostic searches for new physics, developing powerful anomaly detection methods using generative AI techniques such as normalizing flows. These same methods, applied to astronomical data, have enabled discovery of stellar streams and ultra-faint dwarf galaxies. Generative methods also accelerate simulation-based inference across many domains, including gravitational wave detection at pulsar timing arrays. When combined with physics-informed neural networks, generative AI enables an entirely new model-free measurement of the dark matter density in the local Galactic neighborhood. Finally, I will discuss how generative AI in the form of agentic LLMs is creating new modes of human-AI collaboration that promise to further accelerate research. Taken together, these advances signal that we have entered a new and exciting era — one where the oldest questions about our universe may very well be answered with the help of the newest AI tools.

The AdS/CFT correspondence makes it possible in principle to study explicit black hole microstates using field theory techniques. In practice, this is still difficult because the field theory is only dual to gravity when it is strongly coupled. A more modest goal is to focus on supersymmetric black holes which have the minimal energy allowed by their other charges. The superconformal index suggests that many of these are protected from quantum corrections and can therefore be constructed at weak coupling. I will explain how one can identify candidates for black holes by searching the Hilbert space of ABJM theory. On general grounds, these should depend sensitively on trace relations at finite N. This principle, known as fortuity, was first established in a similar study of maximally supersymmetric Yang-Mills theory. I will show that similarities between the two theories extend to the quantitative level thereby allowing additional progress to be made without a brute force search.

The interstellar medium (ISM) plays an important role in sculpting the structure of our Galaxy: in addition to being the birthplaces of stars, ISM substructures have the capacity to significantly perturb stellar orbits. However, conventional stellar-dynamical studies often rely on idealized toy models or omit these gas “fluctuations” entirely, leaving their impact on the evolution of stellar systems poorly understood. In this talk, I will present a model for ISM fluctuations that is both theoretically tractable and easily incorporated into traditional N-body simulations, while retaining the essential features of a realistic ISM. The model is derived from a characterization of the ISM in the state-of-the-art TIGRESS magnetohydrodynamics simulations, which include self-consistent, first-principles gas microphysics and resolve scales down to 2 parsecs. I will then highlight several key differences between the dynamical effects of these realistic fluctuations and those assumed in prevailing models, focusing on orbital heating and radial migration in galactic disks. Finally, I will discuss the importance of accounting for the ISM’s influence in drawing robust conclusions about the Milky Way’s dynamical history and dark matter substructure.