I am broadly interested in statistical physics, with a focus on dynamics in non-equilibrium systems, in both classical and quantum settings.

Recent work has been on Bayesian inference, specifically those on hidden Markov models. In a 'filtering' process, the posterior of the current state is inferred from a history of measurements. For a simple model of inferring a random walk from noisy images, we show that the evolution of the directed polymer formally maps to the Boltzmann probability of the directed polymer, but with an extra delta potential at the signal [1].

Currently, there is a lot of attention on monitored quantum circuits (MRCs), where unitary evolution is combined with measurements. This process is reminiscent of evolving the posterior through a Markov kernel then updating it with a measurement model/likelihood, in Bayesian inference. I am working on the connection between the two problems.

Previous work has been on efficiently simulating dynamics of many-body localised (MBL) systems by exploiting Poincaré-Linstedt perturbation theory to access very long times. We recover some of the hallmarks of MBL such as logarithmically slowly spreading of out-of-time correlators [2].

Personal Website

Publications

• The planted directed polymer: inferring a random walk from noisy images [1]

• Real-time dynamics of one-dimensional and two-dimensional bosonic quantum matter deep in the many-body localized phase [2]

Supervisors

• Curt von Keyserlingk 
• Austen Lamacraft