David Ginsbourger

Biographical sketch:

I am working at the Institute of Mathematical Statistics and Actuarial Science (Department of Mathematics and Statistics, University of Bern, Switzerland) where I am leading a research group and have been teaching a variety of courses. I am a member of the Oeschger Center for Climate Change Research of the University of Bern, serving as associate editor of the SIAM/ASA Journal on Uncertainty Quantification and Technometrics, and as meta-reviewer and area chair of ICML 2022 and NeurIPS 2022, respectively.

I defended my venia docendi (habilitation) in Statistics and Applied Probability before the Faculty of Science of the University of Bern in 2014 and my PhD in Applied Mathematics at the Ecole des Mines de Saint-Etienne in 2009. Previous to that, I obtained a double graduate diploma from Ecole des Mines de Saint-Etienne and Berlin Technical University (2005), a research master's degree in Applied Mathematics jointly awarded by Jean Monnet University and Ecole des Mines de Saint-Etienne (2005), and a licence in Mathematics from Joseph Fourier University, Grenoble (2002).

From 2008/2009, I worked as an assistant and a scientific collaborator, respectively in the Institute of Mathematics and the Centre for Hydrogeology and Geothermics (Stochastic Hydrogeology Group), University of Neuchatel, before starting to work as Senior Assistant (2010) and then Dozent (2014) at the Institute of Mathematical Statistics and Actuarial Science of the University of Bern.

From September 2015 to May 2020, I worked mainly as a permanent senior researcher at Idiap Research Institute where I headed the Uncertainty Quantification and Optimal Design group. During the last two academic years, I lectured at Ecole Polytechnique Federale de Lausanne and participated to the Master in Artificial Intelligence offered by Unidistance and Idiap. I have also held since 2018 a titular professorship at the University of Bern, where I have been employed in several settings since my habilitation.

A significant part of my research deals with Gaussian random field modelling and adaptive design of experiments, with a focus on bayesian global optimization and related topics such as Bayesian set estimation. Further interests include design and estimation of covariance kernels and parameters, as well as connections between Kriging and functional analysis approaches (notably the theory or Reproducing Kernel Hilbert Spaces). From the real-world application side, I have been working with a number of colleagues both from engineering and from geosciences. In recent years, my team and I have started collaborations with climate scientists, and now also with colleagues from medecine.