New at the School: Prof. Dr. Simon Weißmann

The focus of Simon Weissmann’s research is centered on understanding mathematical models based on data, which are typically incomplete and/or perturbed by noise. This includes areas such as Bayesian inference, inverse problems, and data assimilation. Applications span modern machine learning tasks and applied mathematics. Specifically, in the field of uncertainty quantification, one examines the impact of unknown parameters on physical models. These problems are typically highly complex, computationally challenging, and expensive to simulate using numerical solvers.

Simon Weissmann’s research lies at the interface between probability theory, optimization and numerical analysis. In particular, his research focuses on particle-based optimization and sampling methods for solving (Bayesian) inverse problems. More recently, he has shown interest in stochastic approximation methods applied in supervised and reinforcement learning.

Research interest:

• (Bayesian) inverse problems
• Ensemble Kalman filter
• Monte Carlo methods
• Machine learning

Courses:

• Optimization in Machine Learning
• Inverse Problems
• Uncertainty Quantification