Computational Brain Imaging

There is a deluge of data across many scientific disciplines. Future scientific breakthroughs will rely on algorithms to explore these massive data. Our group develops machine learning algorithms to automatically generate scientific discoveries from large-scale datasets comprising thousands of subjects with magnetic resonance imaging (MRI), behavioral, genetic and other physiological measures.

Information processing in the human brain arises from 100 billion neurons connected by 100 trillion synapses into a complex network. MRI allows us to non-invasively study brain networks of living individuals. By exploring large multi-modal datasets, we seek to discover fundamental principles of brain network organization, how brain networks are organized to support cognition and how brain networks are disrupted in mental disorders.

Principal Investigator


YEO Boon Thye Thomas, PhD

Assistant Professor

Electrical & Computer Engineering