The group's main focus is on the application driven development of optimization methods, and their efficient implementation on computers. This comprises nonlinear and integer optimization, as well as optimal control. Uncertainties and machine learning play an important role. A specialization is in numerical algorithms for mixed-integer optimization with differential equations. Most of our mathematical models arise from applications in clinical decision support, energy, and mobility.
Our research is embedded in an ERC consolidator grant, the DFG Research Training Group 2297 Mathematical Complexity Reduction, the Excellence Synergy Programme of Sachsen-Anhalt, the IMPRS Advanced Methods in Process and Systems Engineering and several DFG, BMBF, and industry funded projects.