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This course will introduce some advanced topics in general relativity related to describing gravity in the strong field and dynamical regime. Topics covered include properties of spinning black holes, black hole thermodynamics and energy extraction, how to define horizons in a dynamical setting, formulations of the Einstein equations as constraint and evolution equations, and gravitational waves and how they are sourced.

Topics will include (but are not limited to): - Quantum error correction in quantum gravity and condensed matter - Quantum information scrambling and black hole information - Physics of random tensor networks and random unitary circuits

We suggest a framework for cosmology based on gravitational effective field theories with a negative fundamental cosmological constant, which may exhibit accelerated expansion due to the positive potential energy of rolling scalar fields. The framework postulates an exact time-reversal symmetry of the quantum state (with a time-symmetric big bang / big crunch background cosmology) and an analyticity property that relates cosmological observables to observables in a Euclidean gravitational theory defined with a pair of asymptotically AdS planar boundaries. The latter can be given a microscopic definition using holography, so the model is UV complete. While it is not yet clear whether the framework can give realistic predictions, it has the potential to resolve various naturalness puzzles without the need for inflation.

Zoom Link: https://pitp.zoom.us/j/95099004342?pwd=OWZoanJJMHNzMXF4S3VYM2Vxcklodz09

This course is designed to introduce modern machine learning techniques for studying classical and quantum many-body problems encountered in condensed matter, quantum information, and related fields of physics. Lectures will focus on introducing machine learning algorithms and discussing how they can be applied to solve problem in statistical physics. Tutorials and homework assignments will concentrate on developing programming skills to study the problems presented in lecture.

I will present a brief introduction to Nested Sampling, a complementary framework to Markov Chain Monte Carlo approaches that is designed to estimate marginal likelihoods (i.e. Bayesian evidences) and posterior distributions. This will include some discussion on the philosophical distinctions and motivations of Nested Sampling, a few ways of understanding why/how it works, some of its pros and cons, and more recent extensions such as Dynamic Nested Sampling. If time/interest permits, I can either (a) highlight how this can work in practice using the public Python package dynesty​ or (b) discuss the more general problem of model selection and why Bayesian evidences may (or may not) be helpful.

Zoom Link: https://pitp.zoom.us/j/95990705337?pwd=VzB4cjhzSDhoM0RCYTNwZHUzUVlzdz09

This course will introduce some advanced topics in general relativity related to describing gravity in the strong field and dynamical regime. Topics covered include properties of spinning black holes, black hole thermodynamics and energy extraction, how to define horizons in a dynamical setting, formulations of the Einstein equations as constraint and evolution equations, and gravitational waves and how they are sourced.

Topics will include (but are not limited to): - Quantum error correction in quantum gravity and condensed matter - Quantum information scrambling and black hole information - Physics of random tensor networks and random unitary circuits

This course is designed to introduce modern machine learning techniques for studying classical and quantum many-body problems encountered in condensed matter, quantum information, and related fields of physics. Lectures will focus on introducing machine learning algorithms and discussing how they can be applied to solve problem in statistical physics. Tutorials and homework assignments will concentrate on developing programming skills to study the problems presented in lecture.

Topics will include (but are not limited to): Canonical formulation of constrained systems, The Dirac program, First order formalism of gravity, Loop Quantum Gravity, Spinfoam models, Research at PI and other approaches to quantum gravity.

This course will introduce some advanced topics in general relativity related to describing gravity in the strong field and dynamical regime. Topics covered include properties of spinning black holes, black hole thermodynamics and energy extraction, how to define horizons in a dynamical setting, formulations of the Einstein equations as constraint and evolution equations, and gravitational waves and how they are sourced.