My research focuses on using imaging data to understand how disease affects the brain’s structure. My goal is to build computational technologies to accelerate neuroscience discovery, and to improve clinical trial design and diagnostic accuracy.
One reason this is a hard problem is how many different kinds of neuroimaging data there are. Neuroimages measure anatomy and circuits at the centimeter scale, all the way down to cells and synapses at the micron scale.
Another reason is how complex the geometry of the brain is. It can be hard to even describe the shape of the brain let alone quantify subtle changes attributable to disease.
My work focusses on engineering computational technologies to overcome these challenges, using machine learning techniques to combine information from different imaging systems, differential geometry to describe and quantify structure, and high dimensional statistics to make inferences reliably.