My research interests lie in the fields of computational intelligence and machine learning, primarily in complex structured-data processing (sequences, trees, graphs), deep learning, deep learning for graphs, generative models, Bayesian learning, causal learning and graphical models, continual learning, distributed and embedded learning.

My research activity deals primarily with the design of novel machine learning models and the development of impacting applications exploiting advanced machine learning solutions. Relevant applications of my research include Big Data analysis, biomedical data, machine vision, ambient intelligence, Internet of Things, robotics, autonomous vehicles and computational creativity (visual arts & music).

A (partial) list of my research keywords follows

  • Pervasive artificial intelligence
  • Deep learning & neural networks
  • Generative models
  • Probabilistic & Bayesian learning
  • Deep graph networks/deep learning for graphs
  • Continual/lifelong learning
  • Reinforcement learning
  • Causality
  • Learning/reasoning integration
  • Learning and computing with dynamical systems
  • Neuromorphic/morphological computing
  • Awareness & consciousness in AI systems
  • Compositionality in learning systems
  • Learning in non-stationary settings
  • Ambient intelligence & Robotics