• finite mixture regression models for the number and the costs of claims 

• univariate and multivariate regression models with varying dispersion and shape for claim frequencies and severities 

• copula-based models with regression structures on the mean, dispersion and dependence parameters for different claim types and their associated claim counts and costs 

• dependence modelling in risk management and sensitivity analysis 

• first-order integer-valued autoregressive INAR(1) regression models with varying dispersion for time series of claim counts 

• neural network embeddings of the aforementioned models which are able to capture the stylized characteristics of structured, semi-structured and unstructured insurance data 

• classification of green bonds using statistical learning methods and decarbonization 

• Gaussian process spatial-temporal regression models; and 

• heavy tails and extremes in spatial and temporal settings.