Sun Woo Kim is a PhD candidate in the Department of Physics, King's College London. He is interested in statistical physics, with a focus on dynamics in non-equilibrium systems, in both classical and quantum settings.

His most recent work was on Bayesian inference, specifically on hidden Markov models. In a 'filtering' task, the posterior distribution of the current state is inferred from the history of noisy measurements. For a simple model of inferring a random walk from its noisy images, he showed that the evolution of posterior distribution formally maps to the Boltzmann probability of the directed polymer, a well-studied problem, but with an extra delta potential at the signal location [1].

Recently, there has also been lots of attention on monitored quantum circuits, 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. He is currently working on the connection between the two problems.

My previous work has been on efficiently simulating dynamics of many-body localised systems by exploiting Poincaré-Linstedt perturbation theory to access very long times. In this work we recovered some of the hallmarks of MBL such as logarithmically slowly spreading of out-of-time correlators [2].

Personal Website

Selected 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