Dr. Maja Rudolph

(Bosch Center for Artificial Intelligence, Pittsburgh)
hosted by Machine Learning Group of Prof. Marius Kloft

"Efficient Integrators for Diffusion Generative Models"

Diffusion models have shown remarkable results in generative modeling, but they come with a major drawback: slow sampling at inference time. In this talk, I’ll present two complementary strategies to accelerate sampling in pre-trained diffusion models — Conjugate Integrators and Splitting Integrators. Conjugate integrators generalize DDIM by mapping the sampling dynamics to a space where they can be solved more efficiently.

Bio: Starting in the Fall of 2025, Maja will be an Assistant Professor of Statistics at the University of Wisconsin–Madison, where she works at the intersection of machine learning, probabilistic modeling, and AI. Her research focuses on developing reliable and efficient learning algorithms, with an emphasis on scientific and medical applications. Maja holds a PhD in Computer Science and an MS in Electrical Engineering from Columbia University, and a BS in Mathematics from MIT. Prior to joining academia, she served as Lead Research Scientist at the Bosch Center for AI, where she led technical strategy on foundation models and hybrid modeling, contributing to over 30 patent applications across industrial AI use cases.

Time:	Wednesday, 09.07.2025, 14:30
Place:	Room 48/208

Termin als Datei downloaden und in den Kalender importieren.