Research

This research axis focuses on the study of the relationship between complex biomedical data, such as phenotypes quantified by neuroimaging (such as atrophy, or connectivity) and individual’s -omics profile (e.g. large genetic arrays). To solve this problem, we develop efficient and flexible statistical models for studying multivariate patterns of associations between mutimodal biomedical data.

Selected software and publications:

• Genome to Phenome Sparse Bayesian Regression; Deprez et al, Front Mol Med, 2022
• Functional Prioritization for Imaging Genetics Analysis in Alzheimer’s Disease, Lorenzi et al, PNAS, 2018

I study learning algorithms for modeling and predicting longitudinal changes in imaging, clinical and biological data.
We can think of time series of patients’ data as measurements from an underlying disease progression trajectory spanning several years.
Yet, when we observe the patients during a clinical trial, the disease time information is unknown. This fascinating research topic aims at reconstructing the natural history of a pathology from individual time series of clinical and medical imaging data.

Tutorials:

• Non-parametric disease progression modeling through Gaussian Process Progression Model

Software and papers:

• Gaussian Process Progression Model [Lorenzi et al, NeuroImage 2017; Garbarino et al, IPMI 2019, NeuroImage 2021
• SimulAD [ Abi Nader et al, Brain Comm 2021; Neurobiol of Aging 2022

My research is on the theory and practical use-cases of federated learning (FL), with a special focus in healthcare applications. Current topics include:

• Studying the statistical properties of aggregation mechanisms and clients’ sampling methods in heterogeneity FL setting
• Investigating robust FL methods with respect to clients’ heterogeneity and attacks
• Developing the FL software Fed-BioMed an open source project focused on empowering biomedical research using non-centralized approaches for statistical analysis and machine learning

Selected software and papers:

• Fed-BioMed: federated learning in healthcare; Cremonesi et al, 2024
• Freerider attacks in federated learning;Fraboni et al, AISTATS 2021
• Clustered Sampling in federated learning; Fraboni et al, ICML 2021