Directional Statistics is a branch of the discipline of Statistics that deals with data concerning the circumference, sphere, or Cartesian products thereof with other manifolds. Specific instances of directional data appear in astronomy, medicine, acoustics, image analysis, text mining, environmetrics, and machine learning. The primary challenge encountered when handling this type of data arises from the curvature of the sample space, given that it represents a non-linear manifold. This session aims to stimulate research in Directional Statistics, bring together researchers in the field to share their latest results, and identify interesting new problems and applications.