**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: Advert for a postdoc position in our group

NEWS: 2017 Meeting in Mathematical Statistics (CIRM)

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:

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