Speaker: Tim Garoni (Monash University) Title: The worm algorithm for the Ising model is rapidly mixing Abstract: Markov-chain Monte Carlo methods are a standard tool in statistical mechanics. The key challenge in applying such methods is to develop algorithms which rapidly converge to their stationary state (or "mix"). A number of ingenious Monte Carlo algorithms have been developed over the past few decades, including the Swendsen-Wang, Wolff, and worm algorithms, which seem empirically to mix much faster than traditional local algorithms. In this talk, I'll discuss recent progress in rigorously bounding the mixing time of such algorithms for the Ising model, focussing on the worm algorithm.