Website  ·  Email  ·  Munich, Germany

I am a PhD researcher at LMU Munich, finishing in 2027. My projects move between pixels, catalogues, modeling and parameter inference. I build image simulations, machine-learning models, and statistical analysis.

I am interested in scientific ML and research roles with simulation, systematics, and uncertainty.

FORKLENS uses a two-branch CNN to read a galaxy image and its point-spread function separately. Small neural networks then calibrate the ensemble and learn how much weight to give each object.

Paper: FORKLENS: Accurate weak-lensing shear measurement with deep learning

Overlapping objects create a difficult inference problem: a measurement at one position can contain signal from several sources, and detection itself changes which objects enter the sample. BlendEMU simulates and measures that process; SBSI folds the learned response into likelihood-based inference.

Paper: Emulating redshift mixing due to blending in weak gravitational lensing

Skyvar models the spatial variation of image quality, noise, and extinction across an observed field. It integrates large catalogues through a learned detection model, caches sample selection, models spatial correlations, and propagates the error into parameter inference.

Paper: Anisotropic redshift distributions in photometric galaxy clustering and their cosmological impact