Statistical learning and computer vision

Our research focuses on the study of the theoretical foundations of learning; study and development of learning methods; design and implementation of mathematically sound methods for extracting visual information from images and image sequences.


The group is currently involved in the following projects:

  • FIRB Project for the scientific and technological cooperation Italy-USA: Learning theory and engineering applications. (Partners are MIT and the Universities of Genova and Pavia).
    The project studies scientific problems of learning theory and explores a number of engineering applications. The lab is involved on both theoretical and application aspects, including computer vision and bioinformatics applications.
  • EU Integrated Project Health-e-child, whose main objective is to devise an integrated platform for European paediatrics based on a grid-enabled network of leading clinical centers.
    Within the project the lab is mainly involved in three activities: data processing for the construction of integrated desease models that exploit all the available information levels (medical images, genomic and proteomic data); model analysis and design of data-base guided decision support systems; information fusion and data mining for biomedical knowledge discovery.
  • EU Network of Excellence PASCAL (Pattern Analysis, Statistical modelling and ComputAtional Learning)
    The objective of the network is to build a Europe-wide Distributed Institute which will pioneer principled methods of pattern analysis, statistical modelling and computational learning as core enabling technologies for multimodal interfaces that are capable of natural and seamless interaction with and among individual human users.

More information on our team and our research activity
can be found at the SLIPguru page
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