Starting from Fall 2020, I am an Assistant Professor in the Department of Statistics at Columbia University. Prior to this, I did my Ph.D. at the Technical University of Munich under the supervision of Claudia Klüppelberg and Jean Jacod and spent a couple of years as a postdoc in the group of Robert Dalang at EPFL.
My research interests are primarily focused on the area of stochastic partial differential equations. I am particularly interested in stochastic PDEs driven by Lévy noises, which, in contrast to Gaussian noises, typically have discontinuous and/or heavy-tailed components. In addition to this, I have recently started to work on statistical inference problems for stochastic PDEs, with an emphasis on high-frequency techniques. During my Ph.D. studies, I also worked on topics such as graphical models in finance, mean-field theory, stochastic integration theory, and volatility modeling.
I am a Term Assistant Professor at Columbia University. Before joining Columbia, I obtained my Ph.D. in Mathematical Finance at Carnegie Mellon University, under the supervision of Prof. Johannes Muhle-Karbe. I am mainly interested in stochastic optimization and stochastic differential equations with applications to mathematical finance. I have also worked on various topics in machine learning and data science, including crowdsourcing, dimensionality reduction, and sparse recovery. I was a visiting student at the Simons Institute Foundations of Data Science program.
Anne van Delft
I am a Tenure Track Assistant Professor in the department of Statistics at Columbia University. I obtained my PhD at Maastricht University, the Netherlands, in December 2016. Before joining the Department of Statistics at Columbia university, I held a postdoctoral position in mathematical statistics at the Ruhr University in Bochum, Germany.
My primary research interests lie in the area of stochastic processes that take values in function spaces, and in particular in the development of theory and methodology for function-valued time series with time-dependent characteristics. This is a line of research which I started to develop during my PhD, and is concerned with 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 are omnipresent and can be found in (neuro-)imaging, climatology, genomics, and econometrics. I am especially interested in the development of appropriate statistical theory to further advance inference methods in these essential applications, which are characterized by dependence over time and space, and of which the dependence structure is of an evolutionary nature.
I am currently an Assistant Professor in the Department of Statistics at Columbia University. In summer 2020, I received a Ph.D. from Oxford University under the supervision of Jan Obloj. My research focuses on mathematical statistics with a special emphasis on statistical optimal transport. I am also interested in the robust approach to mathematical finance, which does not start with an a priori model but rather with the information available in the markets. In this context, I have established new connections to the theory of optimal transport on the one hand and robust statistics as well as machine learning on the other, with the ultimate goal to develop a universal toolbox for the implementation of robust and time-consistent trading strategies and risk assessment.