LOT 2 Spring School

Prof. Joost van de Weijer is the leader of the Learning and Machine Perception (LAMP) team at the Computer Vision Center, Universitat Autónoma de Barcelona. His research is dedicated to advancing generative AI, with a focus on two primary research areas: (i) Continual Learning to develop algorithms that can accumulate knowledge from a sequence of tasks over time, (ii) Image Generative Models  investigating on diffusion models, which, guided by text prompts, can generate high-quality, realistic images.

Prof. Egidio Falotico is Professor at The BioRobotics Institute, Scuola Superiore Sant’Anna (Pisa, Italy). Prof. Falotico’s research lies at the intersection of neuroscience, artificial intelligence, and robotics, with a strong focus on brain-inspired robotics, soft robot control, and continual and deep learning. His work aims to understand how movement is conceived, planned, and controlled by the brain and to translate these mechanisms into computational models for robotics. More recently, his research has expanded into the field of continual learning, investigating how robotic systems can acquire and refine skills over time without catastrophic forgetting. By integrating continual learning and deep learning techniques into soft robot control, he aims to develop robots capable of long-term adaptation and resilience, pushing the boundaries of autonomous and intelligent robotic systems.

David Kappel is a Professor at the University of Bielefeld. His scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, he seeks new solutions to problems of information processing in artificial cognitive systems. He draws from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.         

Oriol Pujol Vila is a Full Professor of Computer Science and Artificial Intelligence at the department of Matemàtiques i Informàtica at Universitat de Barcelona. His research focuses on the foundations of machine learning and its application to social challenges, including medical image analysis, ensemble learning, anomaly detection, sequential learning, and deep learning with limited data and multitask supervision. In recent years, his work has shifted toward trustworthy and human-centered AI, including uncertainty estimation, auditing opaque models, bias mitigation, and privacy preservation. His scientific output includes more than two hundred international publications in the field of machine learning and its applications. He has held academic leadership positions such as Head of Studies in Computer Science, Director of the Master’s Degree in Foundations of Data Science, Vice-Rector for Digital Transformation, and Dean of the Faculty of Mathematics and Computer Science at the University of Barcelona.

Prof. Alessandro Betti is Professor of Computer Science within the SySMA research unit of IMT Lucca. His research focuses on studying Machine Learning algorithms for processing data streams that exhibit dynamical properties. From the theoretical standpoint, Prof. Betti proposed and developed a framework rooted in Calculus of Variations for interpreting laws of learning. In the visual domain, Prof.Betti applied these ideas to develop learning agents that utilize deep architectures to extract features that consider motion-induced regularities. Currently, Prof. Betti is working on formulating online continual learning in a principled manner, aiming to investigate the relationship between learning problems over time and optimal control.