Andreas Bergmeister

I am a PhD candidate in AI at TU Munich, in the group of Prof. Dr. Stefanie Jegelka and affiliated with the Munich Center for Machine Learning. I received my Bachelor’s and Master’s degrees in Computer Science from ETH Zurich and interned at the Swiss Data Science Center.

My research is motivated by a long-standing interest in bringing aspects of Kahneman’s System 2 — slow, deliberate, and logical thinking — into AI systems. I aim to make such “slow thinking” more efficient by exploring latent-space reasoning with diffusion and flow-based models, accelerating their training, and aligning them with reward signals to improve reasoning.

Earlier, I worked on geometric deep learning, making graph generation scalable. I also worked on the fundamentals of neural network training, developing PJAX as an alternative to autodiff frameworks for training neural networks without gradients.