Stephan Alaniz is an Assistant Professor in the Multimedia group at Télécom Paris and a Hi! PARIS Fellow. His research focuses on explainable AI and multimodal learning, particularly at the intersection of computer vision, language, and machine learning.

Before joining Télécom Paris, he was a postdoctoral researcher and deputy head of the Explainable Machine Learning group at Helmholtz Munich and the Technical University of Munich. He received his PhD in 2022, with research carried out in part at the University of Amsterdam, the Max Planck Institute for Informatics, and the University of Tübingen, under the supervision of Zeynep Akata and Bernt Schiele. His work has been published in leading AI and computer vision conferences, including CVPR, ECCV, ICCV, ICLR, and NeurIPS.

His Hi! PARIS fellowship project focuses on developing more efficient foundation models through advanced compression methods.

The project aims to make large-scale AI models easier to train, run, and study with fewer computational and financial resources. By drawing on tools from information theory, it seeks to design mathematically grounded compression techniques that preserve as much useful information as possible from the original models. This work addresses a major challenge in current AI research: making powerful foundation models more accessible beyond large engineering teams and high-resource environments.