Physics doctoral researcher at the Institute for Quantum Gravity, FAU Erlangen-Nürnberg.
I work on developing and applying numerical methods for loop quantum gravity, with a focus on deep learning, neural quantum states, and scientific machine learning.
Currently the lead developer of neuraLQX, the largest simulations toolkit for canonical loop quantum gravity, as well as mlx-sparse, which provides sparse array primitives and some sparse linalg for MLX on Apple Silicon.
Interested in fast scalable scode, CUDA applications, GPU performance engineering and profiling, and core/node level performance engineering.