Viktor Stein

Applied mathematician working on Wasserstein gradient flows, regularized divergences, optimal transport, and geometry-inspired machine learning.

I am a PhD candidate in the Applied Mathematics Group led by Gabriele Steidl at TU Berlin working on Wasserstein gradient flows, regularized f-divergences, and regularized optimal transport.

My research develops rigorous analytical foundations and practical algorithms for particle-based sampling methods, aiming to (i) understand properties of gradient flows in probability spaces and (ii) design fast, efficient, and stable algorithms.

Developing methods that are both theoretically grounded and practically robust enables safer, faster, and more interpretable inference methods in modern applications.