Explores the Venice Time Machine project, aiming to digitize Venice's history and present state, creating a digital twin and utilizing advanced technologies for comprehensive monitoring and historical connection.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores data privacy challenges and perspectives in eHealth research, focusing on GDPR compliance, sensitive health data management, and decentralized agents.
Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
