Team

Christos Sakaridis is Lecturer at ETH Zurich and Principal Investigator of the Artificial Visual Intelligence group (AVI) under the umbrella of the Photogrammetry and Remote Sensing lab of Prof. Konrad Schindler, where he leads the TRACE Zurich project. From 2021 to 2025, he also led TRACE Zurich as Principal Investigator at the Computer Vision Lab under Prof. Luc Van Gool, appointed as Postdoctoral Researcher until 2023 and as Established Researcher from 2023 to 2025.

His broad research fields are Computer Vision, Artificial Intelligence and Machine Learning. The focus of his research is on 3D and semantic visual perception, where he develops hybrid, data-driven yet informed, vision models and representations and he emphasizes embodied applications such as autonomous cars and robots. He teaches the Master courses "Computer Vision and Artificial Intelligence for Autonomous Cars" at ETH Zurich and "Computer Vision" at University of St. Gallen.

Christos obtained his PhD in Electrical Engineering and Information Technology from ETH Zurich in 2021, having worked at Computer Vision Lab. Prior to his doctoral studies, he received the MSc in Computer Science from ETH Zurich in 2016 and the Diploma in Electrical and Computer Engineering from National Technical University of Athens in 2014, conducting his Diploma thesis at the CVSP group supervised by Prof. Petros Maragos.

Danilo Đorđević is a PhD student at ETH Zurich, where he is part of the Artificial Visual Intelligence group at the Photogrammetry and Remote Sensing lab (AVI@PRS). His work focuses on vision-language-action models, navigation, computer vision, and self-supervised learning, with the goal of advancing embodied intelligence and robust perception systems.

Vincent van der Brugge is a PhD student at ETH Zurich, where he is part of the Artificial Visual Intelligence group at the Photogrammetry and Remote Sensing lab (AVI@PRS). He is interested in 3D vision for robotics, particularly in learning powerful 3D priors about our world from ubiquitous, unannotated data using self-supervised learning.