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The mathematical structure of large networks and highly symmetric data
is studied in the areas of Graph Theory and Group Theory, respectively.
These theories, both individually and intertwined, give key insights
into the structure of such objects.

The theorems proved at our institute have led to the design of new
algorithms for large networks, and embedding problems in 3-Dimensional
Combinatorics. The theory of local separators we develop here provides a
toolbox of combinatorial methods, which has applications in Data Science
as well as Group Theory.

Group theory is the study of symmetry. At our institute, groups are
being studied from a geometric and topological point of view. The
resulting variety of our methods has led to discoveries in Topological
Dynamics and unveiled new asymptotic phenomena concerning symmetries of
large structures.