Spatio-temporal analysis of biomedical data

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