Filippo Ascolani

Assistant Professor of Statistical Science

My work follows three distinct but related directions: 

(i) modelling complex dependence structures (e.g., time series, multi-samples data...) via a Bayesian parametric and nonparametric approach;

(ii) deep mathematical investigation of the resulting inferential procedures, complemented by the proposal of methods for measuring and tuning dependence and proving frequentist asymptotic properties;

(iii) Rigorous analysis of the computational algorithms employed for posterior inference, with a focus on high-dimensional problems. 

A unifying thread shared by these lines of research is the study of the specific probabilistic structure considered: indeed, the choice of a particular dependence structure (e.g., in hierarchical models), which is often selected through modelling considerations (prior information, domain-specific knowledge, etc.), may lead to substantially different inferential and computational properties.