Anne van Delft joined Columbia University in Fall 2020 as an Assistant Professor of Statistics. Her research focuses on stochastic processes that take values in function spaces, and in particular in the development of theory and methodology for function-valued time series with nonstationary characteristics. This applies to the analysis of sequential collections of data points that themselves come in the form of complex mathematical structures, such as curves, surfaces or manifolds. Examples can be found in (neuro-)imaging, climatology, genomics, and econometrics. Inference techniques to analyze such data not only require a mathematically rigorous and quantitative study of the geometric features and temporal-spatial dependence structure, but moreover must translate into computationally efficient methods. Anne is especially interested in development of appropriate statistical theory to further advance inference methods in cell biology and genomics.
Anne obtained her PhD at Maastricht University, the Netherlands, in December 2016. Before joining the Department of Statistics at Columbia University, she held a postdoctoral fellowship in mathematical statistics at the Ruhr University in Bochum, Germany. She was the recipient of the Itô prize in 2021. Anne is currently Associate Editor for Statistical Inference for Stochastic Processes and for Journal of the American Statistical Association Reviews.
