Dr Robinson's research focuses on the development of computational methods for brain imaging analysis,  and covers a wide range of image processing and machine learning topics. Most notably, her software for cortical surface registration (Multimodal Surface Matching, MSM) has been central to the development of  of the Human Connectome Project’s “Multi-modal parcellation of the Human Cortex “ (Glasser et al, Nature 2016), and has featured as a central tenet in the HCP’s paradigm for neuroimage analysis (Glasser et al, Nature NeuroScience 2016). This work has been widely reported in the media including Wired, Scientific American, and Wall Street Journal). 

Current research interests are focused on the application of advanced machine learning, and particularly Deep Learning to diverse data sets combining multi-modality imaging data with genetic samples. We are particualrly interested in building sensitive models of cognitive development and developmental outcome for prematurely born babies from data collected for the Developing Human Connectome Project (dHCP).