Devis Tuia completed his PhD at University of Lausanne, Switzerland, where he studied kernel methods for hyperspectral satellite data. He then traveled the world as a postdoc, first at University of València, then at CU Boulder and finally back to EPFL. In 2014, he became assistant professor at University of Zurich, and in 2017 he moved to Wageningen University in the Netherlands, where he was chair of the Geo-Information Science and Remote Sensing Laboratory. Since September 2020, he is back to EPFL, where he leads the Environmental Computational  Science and Earth Observation laboratory (ECEO) in Sion. There, he studies the Earth from above with machine learning and computer vision.

Yuki M. Asano leads the Fundamental AI (FunAI) Lab at the University of Technology Nuremberg as a full Professor, having previously led the QUVA lab at the University of Amsterdam, where he collaborated with Qualcomm AI Research. He completed his PhD at Oxford's Visual Geometry Group (VGG), working with Andrea Vedaldi and Christian Rupprecht. His lab conducts research at the cutting edge of computer vision and machine learning, particularly self-supervised and multimodal learning. He has recently promoted an ELLIS scholar, and he has served as area chair and senior area chair for top conferences including NeurIPS, ICLR, and CVPR, and organizes workshops and PhD schools including the ELLIS winter school on Foundation Models.  

Ira Assent is full professor of computer science at Aarhus University, Denmark, where she heads the Data-Intensive Systems research group, and the Big Data Analysis research within the DIGIT Aarhus University Centre. She is a co-lead of the Collaboratory Causality & Explainability in the Pioneer Center for Artificial Intelligence Denmark. She is a (part-time) director of the Institute for Advanced Simulation, Data Analytics and Machine Learning (IAS-8) at the Forschungszentrum Juelich, Germany. She received the PhD degree from RWTH Aachen University, Germany, in 2008. Her research interests include explainable AI, unsupervised and supervised learning, efficient and scalable algorithms for data analysis and data management.