MrPhilipp Wacker

My research lies at the intersection of applied mathematics, statistics, and computational science, with a focus on inference and optimisation for complex dynamical systems under uncertainty.

A central theme is the mathematical analysis and algorithmic development of Bayesian inverse problems. I am interested in well-posedness, posterior consistency, and structure-preserving approximations in both finite- and infinite-dimensional settings. My work combines rigorous analysis with scalable computational methods for high-dimensional and nonlinear models.

 

Core Research Themes

 

Bayesian Inverse Problems and Uncertainty Quantification

• Measure-theoretic foundations of Bayesian inversion
• Posterior approximation in high dimensions (sampling)
• Rare-event estimation and evidence computation

 

Particle-Based and Derivative-Free Methods

• Consensus-based optimisation
• Ensemble and interacting particle systems for sampling and optimisation

 

Optimal Experimental Design

• Information-theoretic design criteria
• Sequential and adaptive design
• Design for nonlinear and dynamical models
• Coupling experimental design with inverse problems