Our research focuses on developing computational methods for uncertainty quantification and statistical inverse problems. The goal is to optimally account for uncertainties in complex (PDE) models in computational science and engineering. To this end, we leverage theory and methods from mathematics and statistics including numerical methods for PDEs, statistical learning methods, and optimisation. The applications include nanoelectronics, medical imaging, biology and medicine.