Jonathan Geuter
Ph.D. Student in Applied Mathematics
Kempner Graduate Fellow
Contact Information
Areas I Research:
About
Jonathan Geuter is a Ph.D. student in the ML Foundations group advised by David Alvarez-Melis. Previously, he received a BSc and MSc in Mathematics at TU Berlin and spent a year at UC Berkeley.
Research Focus
Geuter’s research is in deep learning, with a focus on optimal transport for machine learning and on generative models, particularly LLMs. He approaches these areas from both theoretical and empirical perspectives to develop efficient, principled machine learning algorithms.
On the optimal transport side, he develops scalable neural OT methods with theoretical control. On the LLM side, his current work targets efficient test-time scaling and distillation. More broadly, his interests span generative modeling across modalities, including image generation.