One aim of this workshop is to build connections between Topological Data Analysis on one side and Manifold Learning on the other. The moment has been reached when the mathematical, statistical and algorithmic foundations of both areas are mature enough -- it is now time to lay the foundations for joint topological and differential geometric understanding of data, and this discussion will explicitly focus on this process.

The second aim is to bring GDA closer to real applications. We see the challenge of real problems and real data as a motivator for researchers to explore new research questions, to reframe and expand the existing theory, and to step out of their own sub-area.