Biostatistics for Spatial and High-Dimensional Data

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With the rapid advancement of modern biotechnology, the world is witnessing an explosion of data. This wealth of information represents an abundant resource for tackling many of the most fundamental scientific questions, for example, questions about how the brain works, or how genetic variation influences the brain. Despite this, a major challenge facing the scientific community is the sheer size and complexity of modern datasets, which has created a pressing need for rigorous statistical approaches to understand it. Neuroimaging studies can involve large datasets describing the anatomy, function, and connectivity of the brain, while studies in imaging genomics involve a combined analysis of brain images and additional data arising from high-throughput genotyping. It is not unusual to conduct well over one billion tests of association in a brain-wide genome-wide study. Motivated by the challenging statistical problems arising in these areas, this research will develop new methodology for spatiotemporal analysis in high-dimensional settings.