In this workshop we will bring together physicists, computer scientists and practitioners from related fields to share knowledge and collaborate on the development, analysis and application of new algorithms. We will particularly emphasize algorithms applied to systems studied in statistical physics because of their simple formulation and wide relevance to many other scientific fields. A paradigmatic example is the Ising model and its many variants. Originally designed to model magnetic systems but now applied to fields as diverse as image analysis and opinion formation in social systems, Ising systems are simply defined but display a rich array of complex behavior induced by many-body interactions. Because most complex systems studied in statistical physics are stochastic we will focus mainly, though not exclusively, on Monte Carlo algorithms, i.e., algorithms that rely essentially on pseudorandomness for their operation.