Isaac is interested in developing new computational methods that leverage biological knowledge to address open questions related to mental health and psychiatric disorders. In particular, Isaac’s work seeks to characterize and predict the spectrum of psychotic disorders by using machine learning to combine multiple types of neuroimaging-derived brain connectivity as well as genetic and other biological data.

Related Publications

Multimodal Graph Coarsening for Interpretable, MRI-Based Brain Graph Neural Network

Isaac Sebenius, Alexander Campbell, Sarah E. Morgan, Edward T. Bullmore, Pietro Liò

IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), :