Markov Chain Monte Carlo (MCMC) methods are of fundamental importance to various scientific fields, such as statistics, statistical physics,
molecular dynamics and machine learning. The usefulness of these MCMC methods depends for a large part on their computational efficiency. Non-reversible Markov chains may have significant benefits in terms of computational efficiency over their reversible counterparts. It will be the aim of this workshop to explore: theoretical properties of non-reversible Markov chains that are important in determining their usefulness for MCMC; aspects of augmented state space algorithms such as Hamiltonian Monte-Carlo and their relations to non-reversible Markov processes; theory and practice of design and implementation of efficient non-reversible Markov chains.