Group leader: Prof. Dr. David Ginsbourger
Spatial statistics essentially deals with the description and modelling of events occuring accross multivariate spaces, and has been used in a variety of domains from geosciences and beyond. Models from spatial statistics have been leveraged within the blossoming field of Uncertainty Quantification in order to reconcile observational and simulation data towards enhanced probabilistic predictions and targeted sequential data acquisition strategies. We investigate random field models as well as distance and kernel methods for prediction and sequential design algorithms dedicated to optimization, set estimation, and further inverse problems. Application domains include environmental geosciences, reliability and safety engineering, atmospheric sciences, agronomy, robotics, machine learning, and, increasingly, biomedical sciences.