We use probabilistic generative models to analyse data for clinical research and applications. Our aim is to obtain an interpretable probabilistic model of the data to facilitate downstream tasks in the clinical domain. To this end, we work on several projects – on deep generative models for spatio-temporal modeling of neuroimaging data to study disease progression in neurodegenerative diseases such as Alzheimer’s; on dynamical systems for single cell mRNA sequence data to simulate different interventions and extract biological hypothesis; on interpretable probabilistic machine learning models for physiological time series to impute missing values and predict adverse events.