We develop methods for the statistical analysis of fMRI time series data, determining distributed networks that correspond to brain function, and making predictions about psychological or disease states. Recent work has focused on developing new methods for assessing time-varying connectivity, developing neurological signatures for predicting physical pain, and using information from group-level studies to perform successful single-subject inference. In addition, we have developed a series of on-line courses on fMRI data analysis freely available on the Coursera platform.
Professor of Biostatistics
Specialization: Statistical Analysis of Neuroimaging Data
Our research focuses on mathematical and statistical problems relating to functional Magnetic Resonance Imaging (fMRI).
To advance neuroscience discovery by uniting neuroscience, engineering and computational data science to understand the structure and function of the brain.