Unit Jena: ELLIS Summer Lecture Series - Explainability & Understanding of Models

The ELLIS Summer Lecture Series on “Explainability & Understanding of Models” brings together leading researchers to explore one of the most pressing challenges in modern AI: making complex machine learning systems transparent, interpretable, and trustworthy. As models grow in scale and impact, understanding how and why they make decisions becomes essential for scientific progress, real-world deployment, and societal acceptance. This lecture series offers cutting-edge insights into methods for explaining model behavior, uncovering internal representations, and bridging the gap between performance and interpretability—providing a platform for discussion, learning, and collaboration across the ELLIS community and beyond.

The lecture series is supported by the Professorinnenprogramm 2030 funded by the German federal and state governments as well as by the “Women in ELLIS” initiative. The Professorinnenprogramm 2030 promotes gender equality and supports the advancement of women in academia and research institutions across Germany. As part of the Professorinnenprogramm 2030, Friedrich Schiller University Jena received a positive evaluation of its gender equality concept by an independent review panel and was recognized as a “University with Strong Gender Equality” (“gleichstellungsstarke Hochschule”). “Women in ELLIS” highlights and connects leading female researchers in artificial intelligence and machine learning across Europe. Through lectures, mentoring, and networking activities, the initiative aims to strengthen diversity and female leadership in AI research.

TITLE
Toward Generative Models that Understand the Visual World

ABSTRACT
Despite remarkable advances, visual generative models are still far from faithfully modeling the world, struggling with fundamental aspects such as spatial relations, physics, motion, and dynamic interactions.
In this talk, I present a line of work that tackles these challenges, based on a deep understanding of the inner mechanisms that drive models. I will begin by analyzing state-of-the-art visual generators, gaining insights into the underlying reasons for their limited understanding. Building upon these insights, I will demonstrate methods that significantly enhance both spatial and temporal reasoning in image and video generation, surpassing even resource-intensive proprietary models without relying on additional data or model scaling. I will conclude the talk by discussing open challenges and future directions for advancing faithful world modeling in visual generative models.

BIO
Dr. Hila Chefer is a Research Scientist at Black Forest Labs and an incoming Assistant Professor (Senior Lecturer) at Tel Aviv University. Her research focuses on architecture development and interpretability for visual foundation models, aiming to both understand and advance their generative capabilities.