LECTURERS and STAFF

Battista is Full Professor at University of Cagliari, Italy, IEEE Fellow and Co-Founder of Pluribus One. Battista Biggio’s research over the past 15 years has addressed theoretical and methodological issues in the area of machine learning and pattern recognition, in the context of real-world applications, including spam filtering, intrusion and malware detection, and biometric recognition. He has provided pioneering contributions in the area of adversarial machine learning, demonstrating gradient-based evasion and poisoning attacks on machine-learning algorithms, and how to mitigate them, playing a leading role in the establishment and advancement of this research field. Battista received the 2022 ICML Test of Time Award for the paper “Poisoning Attacks against Support Vector Machines” (ICML 2012).  

Nicolò Cesa-Bianchi is professor of Computer Science at the University of Milan, where he is currently head of the Computer Science programs. He was President of the Association for Computational Learning and member of the steering committee of the EC-funded Network of Excellence PASCAL2. He served as action editor for the Machine Learning Journal, for IEEE Transactions on Information Theory, and for the Journal of Machine Learning Research. He is currently associate editor for the Journal of Information and Inference. He was program chair of the 13th Annual Conference on Computational Learning Theory and of the 13th International Conference on Algorithmic Learning Theory. He has held visiting positions at UC Santa Cruz, Graz Technical University, Ecole Normale Supérieure in Paris, Google, and Microsoft Research. His main research interest is the design and analysis of machine learning algorithms, with special emphasis on sequential learning problems. He is co-author of the monographs, Prediction, Learning, and Games, and Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. He is recipient of a Google Research Award and of a Xerox Foundation UAC Award. 

Elisa Ricci is a Professor at the Department of Information Engineering and Computer Science (DISI) at University of Trento and the Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. Elisa is also the Coordinator of Doctoral Program in Information Engineering and Computer Science at University of Trento. She is an ELLIS and a IAPR Fellow.

Her research lies at the intersection of computer vision, deep learning and robotics perception. She is interested in developing novel approaches for learning from visual and multi-modal data in an open world, with particular emphasis in methods for domain adaptation, continual and self-supervised learning.

Antonio is a principal investigator (PI) at the ELLIS Institute Tübingen and independent group leader the MPI for Intelligent Systems. He leads the Deep Models and Optimization group, and he is a lecturer at the University of Tübingen and faculty for CLS, ELLIS, IMPRS-IS PhD Programs.

His goal is to improve efficiency and accessibility of deep learning technologies in science and engineering by pioneering new architectures and training techniques grounded in theoretical knowledge. His work encompasses two main areas: understanding the intricacies of large-scale optimization dynamics and designing innovative architectures and powerful optimizers capable of reasoning in complex data. Central to his studies is exploring innovative techniques for decoding patterns in complex sequential data, with implications spanning biology, neuroscience, natural language processing, and music generation.

Marco Gori is professor of computer science at the University of Siena and 3IA chair at Université Côte d’Azur, Nice (MAASAI lab https://team.inria.fr/maasai/team-members/).  His research interests are in the field of artificial intelligence, with emphasis on machine learning, vision, and game playing. In the last few years, he has been mainly involved in the unification of computational processes of reasoning and learning. He's mostly driven by the principle that the emergence of cognition is rooted in natural laws of computation.  He also very much like discussions on novel models of computation and their relationships with human brain. 

I am a professor at EECS Department at UC Berkeley, where I am part of the Berkeley Artificial Intelligence Research Lab (BAIR). Before that, I spent a decade on the faculty of the Robotics Institute at CMU. I am also still remembered in lovely Oxford, where I did a post-doc with Andrew Zisserman. During 2007-2015, I have also been closely collaborating with Team WILLOW at École Normale Supérieure / INRIA in beautiful Paris. Over the years, I was lucky to have some amazing officemates. The central goal of my research is to use vast amounts of unlabelled visual data to understand, model, and recreate the visual world around us. My research has been mainly in data-driven computer vision, as well as its projection onto computer graphics and computational photography. In the last five years, my lab has been at the forefront of reviving self-supervised learning. Other interests include human vision, visual data mining, robotics, and the applications of computer vision to the visual arts and the humanities.