Research Programs

Several working groups of the Institute of Mathematics take part in the following coordinated research programs:

SFB 1294 "Data Assimilation"
The Collaborative Research Center SFB 1294 "Data Assimilation" is being funded by the German Research Foundation (DFG) since 2017. Collaborating institutions are University of Potsdam, Humboldt University Berlin, GFZ Potsdam, TU Berlin and WIAS Institute Berlin. The program is coordinated at the Institute of Mathematics at the University of Potsdam.

SPP 2026 "Geometry at Infinity"
The Priority Program 2026 "Geometry at Infinity" is being funded by the German Research Foundation (DFG) since 2017. Researchers from more than 20 German and Swiss universities take part in this program. The program is coordinated at the Institutes of Mathematics at the Universities of Augsburg and Potsdam.

Research Group Linkage: Singular diffusions: analytic and stochastic approaches
This Research Group Linkage between the Institute of Mathematics of University Potsdam and the Institute of Mathematics at the National Academy of Sciences of Ukraine is funded by the Alexander von Humboldt Foundation. This programme allows sponsorship by the Alexander von Humboldt Foundation of long-term research collaborations between academics in Germany and abroad.

PharMetrX
PharMetrX is an interdisciplinary PhD program of Freie Universitaet Berlin and the University of Potsdam at the interface of pharmacy & mathematics. It is supported by an international consortium of research-driven pharmaceutical companies. PharMetrX's research and training is in the areas of pharmacometrics & computational disease modelling, with applications in drug research & development as well as in therapeutic use.

Project ASCAI
The project ASCAI concerns unsupervised learning, in a batch and a sequential setting. Unsupervised learning is a core problem of Artificial Intelligence in general, and machine learning in particular. Despite its pervasive nature, many important questions remain open, in particular in complex problems that go beyond vanilla clustering. In ASCAI, we depart from recent progresses in clustering and aim at extending these results to complex unsupervised learning problems, both in a batch and in a sequential setting.