Resarch Group in Mathematical Statistics
`Uncertainty Quantification and Modern Statistical Inference (UQMSI)'
Our research group investigates the mathematical underpinnings of modern statistical inference problems, with a particular focus on uncertainty quantification methodology (such as confidence statements and tests) in high- and infinite dimensional models. The mathematics involved lies at the intersection of theoretical statistics, probability theory and functional analysis. Currently (2015-2020) we are supported by a Consolidator Grant of the European Research Council (ERC).
NEWS:
June
2017 Workshop on Statistical foundations of
uncertainty quantification for inverse
problems
Research topics include:
nonparametric statistics
confidence regions for high-dimensional statistical models
Bayesian nonparametrics
statistics for stochastic processes and inverse problems
concentration of measure and empirical process theory
Current group members:
Former group members:
Postdocs: A. Carpentier, K. Lounici, J. Soehl, B. Sriperumpudur
Phd students: A. Bull, A. Kueh, K. Ray
Past activities:
2017 YES VIII Workshop on Uncertainty Quantification
Course on Concentration Inequalities by Stephane Boucheron
2015 Oberwolfach Workshop Probabilistic Techniques in Modern Statistics
