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 analysis. Currently (2015-2020) we are supported by a Consolidator Grant of the European Research Council (ERC), and also by CCIMI and CCA.
NEWS: 2018 Uncertainty quantification in complex, nonparametric statistical models (Lorentz Centre)
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:
Some former group members:
Alexandra Carpentier, Adam Kashlak, Karim Lounici, Jakob Söhl, Kolyan Ray, Bharath Sriperumpudur, Elodie Vernet
Past activities:
2017 Meeting in Mathematical Statistics (CIRM)
June 2017 Workshop on Statistical foundations of uncertainty quantification for inverse problems
2017 YES VIII Workshop on Uncertainty Quantification
Course on Concentration Inequalities by Stephane Boucheron
2015 Oberwolfach Workshop Probabilistic Techniques in Modern Statistics