Our research aims at enabling machines to mimic human-like intelligence, via models and algorithms to process vast amounts of data, recognize patterns, and make decisions, using techniques from machine and deep learning as well as symbolic reasoning.
In our research activities, we cover all modalities, including natural language processing and computer vision. We focus especially on (i) explainable AI, (ii) deep learning with logical constraints for safe AI , (iii) hybrid model-based approaches to explainable, fair, and robust AI, (iv) general AI via predictive coding and active inference, and (v) intelligent applications, such as in healthcare and law.
