My research interests focus on the development and implementation of methods that allow the probabilistic investigation of complex system dynamics. Basically, I am working in the following four overlapping methodological domaines, often in the context of time-consuming determinisitic simulation codes and system observations or models consisting of a large amount of data.

Combining machine learning algorithms to perform

- regression tasks to estimate
**meta models**of complex system dynamics, e.g. via**Gauss Processes** **adaptive sampling**approaches to investigate complex system behavior

Uncertainty & Sensitivity Analysis

Using advanced analysis approaches to investigate complex systems with focus on system safety and risk measures

- via
**Monte Carlo Dynamic Event Trees (MCDET)**to exploratively study and probabilistically evaluate the impact of stochastic state transitions on the system dynamics - via Uncertainty Analysis methods, i.e. estimation of
**Tolerance Limits**via Wilks Approach,**Bootstrapping**or classical**Monte Carlo Simulations** - via Sensitivity Analysis methods to identify major contributors to the response uncertainty of a system, i.e. by squared multiple correlation coefficient or
**Sobol Indices**

Complex Network Analysis

Using network analysis and representation approaches to investigate

- complex dependency patterns, e.g. multiple
**hazard impacts**on systems - dynamic patterns of
**probablisitsc risk models**, e.g. of critical event sequences

Bayesian Inference

Developping Bayesian approaches to detect and characterize multiple **transition patterns** in complex time series, such as paleo-climate observations