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Our research covers four main areas:

  • Computational Biology and Biostatistics

    Faculty: Annalisa BarlaAlessandro Verri

    Description: Our goal is to study and develop computational methods and models for the understanding of biological processes. The starting point is often derived from our work in learning theory and algorithms with particular emphasis on the problems related to the role played by prior knowledge and the integration of data coming from different sources and contexts.

  • Computer Vision

    Faculty: Francesca Odone

    Description: We explore the multifaceted world of image and scene understanding and reconstruction combining computer vision and statistical learning ingredients. Computer vision methods are used to extract information from the visual signals, while we resort to statistical learning to model variability, and to gain robustness and flexibility.

  • Learning Theory and Algorithms

    Faculty: Ernesto De Vito, Lorenzo Rosasco, Alessandro Verri

    Description: We focus on the mathematical aspects of Learning Theory to the purpose of developing algorithms which can effectively learn the solution to a given problem from small samples. Our approach is based on the theory of regularization of ill-posed inverse problems and uses methods from functional analysis, convex analysis, and non-parametric statistics.

  • Medical Image Analysis

    Faculty: Alessandro Verri

    Description: In this area we investigate the problems arising in the analysis, both automatic and quantitative, of medical images. Our interests range from the process of image acquisition to the development of methods for dealing with massive amount of data and the extraction of knowledge from images. Our work, often carried out in close collaboration with radiologists and clinicians, aims at the development of software systems which can be useful in the clinical practice.

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