Various connections between information and computation theory are studied since many decades. A notion of algorithmic (Kolmogorov) complexity that uses computation theory to measure information in finite objects (instead of random variables) appeared in 1960s and became by now a standard tool. There are many examples of problems where information-theoretic tools like Shannon’s entropy were used to analyze complexity of algorithms and prove lower 17  bounds. Now, several directions of research based on theses connections are currently active: communication and information complexity, complexity and dimensions, complexity vs compression, pseudo-random structures, complexity of computation and Kolmogorov complexity, and randomness and computability theory. The goal of this week is to gather experts from all of these directions, who share a common general view on randomness, information and complexity.