I like using generative models to extract information in Bayesian inference problems on fundamental physics in cosmology.
Right now i'm working on
- a simulation-based inference (SBI) package in
jax, - field-level inference pipelines using generative models.
I'm interested in generative models...
- Score-based diffusion models,
- Variational Diffusion models (VDMs),
- Variational Diffusion Autoencoders (VDAEs).
...transformer models & geometric deep learning in general...
...and statistical problems in general
I also teach MSc Physics students in the Physik x AI labs at LMU Physik where I write teaching material that delivers machine learning insights derived from problems in physics.
My goal is to show students cutting edge algorithms and statistical methods that they will not learn anywhere else.
- Simulation-based inference with random fields in cosmology,
- Generative models for Bayesian inference in cosmology (link not available).