Learning continually from a never-ending stream of data is a key property for every Pervasive AI system. Being able to sustainably and effectively adapt to the ever-changing environments and circumstances of the real-world is often defined as a key property of Intelligence.
At the PAI Lab we design and implement deep continual learning algorithms for enabling the next generation AI systems and study their applications to real-world problems. In particular, we are interested in:
- Unsupervised / Self-Supervised/ Weekly/ Semi-Supervised Continual Learning
- Continual Sequence Learning
- Continual Learning and Fundation Models
- Neuroscience-Inspired Continual Learning
- Continual Reinforcement Learning
- Continual Learning R&D Frameworks & Tools
- Continual Robot Learning
- Continual learning at the Edge
- Distributed Continual Learning
- Real-World Continual Learning Applications
- Continual Learning as a Service (CLaaS)
- …and much more!
Team
- Davide Bacciu – Associate Professor
- Vincenzo Lomonaco – Assistant Professor
- Claudio Gallicchio – Assistant Professor
- Lucia Passaro – Assistant Professor
- Antonio Carta – Assistant Professor
- Daniele Mazzei – Assistant Professor
- Julio Hurtado – Post-Doc
- Andrea Cossu – PhD Student
- Rudy Semola – PhD Student
- Michele Resta – PhD Student
- Valerio De Caro – PhD Student
- Reshawn Ramjattan – PhD Student
- Hamed Hemati – PhD Student (co-supervised with Damian Borth at University of St. Gallen)
- Edoardo Urettini – PhD Student (co-supervised with Fabrizio Lillo at Scuola Normale Superiore)
Current Visitors
- Andrii Krutsylo, PhD Student at Polish Academy of Sciences
- Bilge Celik, PhD Student at Eindhoven University of Technology
- Angelo Menezes, PhD Student at University of São Paulo, Brazil
Past Visitors
- Pablo Ferri Borredà – PhD Student at University of Valencia
- Ghada Sokar – PhD Student at Eindhoven University of Technology
Continual learning course
Check out our online lectures and resources on the ContinualAI community