Interpretable Machine Learning

Quantify the uncertainty in automatic anatomical landmark localisation

Model both second-order and third-order structures simultaneously for complex networks

Classify autism across multiple sites via site-dependence minimisation & second-order functional connectivity

Interpretable machine learning to improve prognostic and treatment response assessment on cardiac MRI

Construct a feature-importance-aware and robust aggregator for graph convolutional networks

Learn low-dimensional representations of high-dimensional data from their natural tensors