Robust Solutions under Uncertainty

Uncertainty quantification and visualization have been entering ZIB’s research in a wide range of application areas from logistics to medicine.

We aim at developing new methods for robust optimization and design under uncertainty (e.g., for fault tolerant telecommunication networks, delay resistant timetables, robust surgery schedules, or robust therapy design) and for the incorporation of uncertainty concepts in data-driven inverse problems (e.g. sparse classification, parameter estimation, or feature extraction).