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Machine Learning for Data Science

  • Our main focus is to apply machine learning to real-world data, making sure to design stable, reproducible and interpretable applications with an impact in real-life scenarios.

    To this aim, we design and use machine learning methods that are based on:

    • regularization methods
    • deep learning
    • statistical modeling
  • The main goal is to analyze complex, temporal, and possibly structured data in order to:

    • identify predictive signatures
    • discover interpretable patterns
    • infer and analyze networks


  • Deconstruct and rebuild phenotypes: a multimodal approach toward personalized medicine in ALS (DECIPHER-ALS)
    Annalisa Barla - Head of unit

    Start 2019

    End 2021

  • Liguria 4P Health: Predictive, Personalised, Preventive, Participatory healthcare
    Alessandro Verri - Principal Investigator
    POR-FESR Regione Liguria

    Start 2018

    End 2020

  • UniGe Web - Data driven web redesign
    Annalisa Barla - co-Principal Investigator

    Start 2017

    End 2020

  • Advancing non-invasive procedures for the support of early diagnosis of partial epilepsies
    Annalisa Barla - Principal Investigator
    Compagnia di Sanpaolo

    Start 2018

    End 2020

  • FINSEC: tecnologie di machine learning per la finanza
    Alessandro Verri - Principal Investigator
    GFT - GFT Technologies

    Start 2018

    End 2019


  • 2019

    Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network
    S. Fiorini F. Hajati A. Barla F. Girosi
    PloS one

  • Multi-parameters Model Selection for Network Inference
    V. Tozzo A. Barla
    International Conference on Complex Networks and …

  • 2018

    Data-driven continuous assessment of frailty in older people
    C. Martini A. Barla F. Odone A. Verri A. Cella
    Frontiers in Digital …

  • Latent variable time-varying network inference
    F. Tomasi V. Tozzo S. Salzo A. Verri
    Proceedings of the 24th ACM …

  • A Visual Computing Approach for Estimating the Motility Index in the Frail Elder.
    C. Martini N. Noceti M. Chessa A. Barla
    VISIGRAPP (5 …