Research Interests


My main research interests relate to the broad areas of Computational Intelligence and Machine Learning, in particular regarding bio-inspired models of learning and perception as well as probabilistic learning models with application to biomedical data processing and image understanding.

The focus of my PhD research (thesis defended in 2008) has been on the hierachical processing and indexing of annotated image collections by an hybrid neuro-probabilistic approach. Previosly, as part of my undergraduate and early graduate research activity I've been working on intelligent models for robotic platforms, in particular as regards motor control.

Recently, I've started focusing my research on unsupervised learning models for structured processing (work still in progress). Further, I am actively pursuing research on computational intelligence approaches in service-oriented architectures, including wireless networks access control and web-services contract negotiation.

A (partial)) list of my research keywords follows
  • Neural networks
  • Unsupervised learning and feature extraction
  • Informed clustering
  • Probabilistic graphical models
  • Structure finding in Bayesian Networks
  • Machine vision and image understanding
  • Fuzzy decision making
  • Biomedical data analysis (DNA microarray and proteomics)
  • Structured data processing