Complex systems can be found in many different areas of business, science and engineering. They are usually characterized by the nonlinear interaction of many (microscopic or mesoscopic) components and can exhibit a variety of structures on the macroscopic level. These structures can be spatial, temporal or spatio-temporal in nature and can be captured quantitatively. This can result in large amounts of multivariate data. For many applications, it is important to interpret these datasets using classifications and macroscopic descriptions in order to make predictions and/or to better understand the underlying mechanisms. To achieve this, we focus on the development and application of dimension reduction techniques and their use in an AI context.

Prof. Dr. Christian Uhl