Postdoctoral researcher · University of Tübingen
I develop probabilistic methods for simulation and inference in dynamical systems, from Kalman filters in millions of dimensions to generative models of the climate. Lately, I am drawn to a new question: what these tools can tell us about social dynamics.
Together with Philipp Hennig and the Methods of Machine Learning group in Tübingen.
Bayesian filtering and smoothing for high-dimensional systems, and probabilistic numerical solvers for ODEs and PDEs. The recurring theme: exploit structure (low rank, Kronecker, sparsity) so that principled uncertainty quantification scales to problems that matter.
Diffusion models that turn coarse climate simulations into spatiotemporally coherent, probabilistic weather-scale fields. Simulation output that comes with calibrated uncertainty instead of a single guess.
A new direction, currently in the process of sharpening the focus. Current direction: heterogeneous-agent models of wealth and income, and the (stochastic-process) machinery behind them. Interested both in the application and in the methodology. Happy to chat about both, anytime!
