Talks

The AdS/CFT duality has been extremely useful in understanding quantum gravity. Tensor networks are a toy model of holography that have provided valuable lessons that have been imported to AdS/CFT. In particular, random tensor networks have a quantitative connection with fixed-area states in the gravitational path integral. This has allowed for a translation of various tensor network results to gravity that I will discuss, including an understanding of the Renyi entropy, entanglement negativity, entanglement wedges for bulk regions and resolving entropic paradoxes using wormholes.

 In this talk I'll explain how to take a well-defined flat-space limit of brane models of AdS coupled to a non-gravitating bath. In the dual BCFT this amounts to a triple-scaling limit where both the number of boundary degrees of freedom and the boundary coupling are taken to infinity while the BCFT boundary piecewise approaches a lightcone. The resulting construction offers a new approach to study the relation between the flat limit of AdS and Carrollian/Celestial CFT, as well as the potential to carry over ideas from AdS/CFT to flat space. I will discuss some implications for entanglement entropy in flat space, in particular the fact that two different notions of entanglement entropy arise on scri, depending on whether one traces out modes which have left through scri.

Galaxy clusters are visible across the electromagnetic spectrum. Observations of their structure, abundance, and evolution provide constraints to cosmology. We are in a golden age of statistical power for galaxy clusters, where observations will provide multiwavelength data for tens of thousands of galaxy clusters. However, our ability to maximize the use of clusters as cosmology probes is limited by how well we measure cluster masses and quantify systematic effects in galaxy cluster detection and characterization. I will discuss modeling efforts that enable us to test for systematics that arise from both astrophysical and observational effects.

Bio for Luis Serrano:

Luis Serrano did his undergraduate and masters in math at the University of Waterloo, his PhD in algebraic combinatorics at the University of Michigan, and a postdoctoral fellowship at the University of Quebec at Montreal. He then went to industry, working in AI at Google, Apple, in large language models at Cohere, and in quantum AI at Zapata Computing. He has taught courses in AI at platforms like Udacity and Coursera, and hosts the popular YouTube channel Serrano Academy with over 180 thousand subscribers. He’s also the author of the Amazon bestseller Grokking Machine Learning.