Yida Wang will present his Pre-FPO on Tuesday, November 24, 2015 at 9am in CS 401. The members of his committee are: Kai Li (adviser), Jonathan Cohen (Princeton Neuroscience institute, reader), Nicholas Turk-Browne (Princeton Neuroscience institute, reader), Olga Troyanskaya (non-reader), Sebastian Seung (non-reader). Everyone is invited to attend his talk. The talk title and abstract follow below. Title: Exploring Brain Interactions Using Full Correlation Matrix Analysis Abstract: Functional magnetic resonance imaging (fMRI) measures human brain activity by detecting changes associated with blood flow. Neuroscientists have been analyzing fMRI data to study human brain by mostly focusing on the activities of brain regions. This dissertation proposes, designs and implements full correlation matrix analysis (FCMA) to study whole brain interactions in an unbiased way using a compute cluster within a tractable time. Traditional exhaustive full correlation computations would takes years to complete on a modern computer server. In this dissertation, we propose algorithm optimizations and implementation optimizations to speedup by orders of magnitude. In addition, we optimize the most computationally intensive component of FCMA, the voxel selection, on Intel Xeon Phi coprocessors to finish the whole process in seconds. We also adapt FCMA algorithms to the online fashion and build a real-time fMRI system to analyze the interactions of the whole brain interactively to provide the neurofeedback in real time. For offline analysis, our FCMA toolbox finishes a typical brain interaction study in a few minutes using 48 compute nodes; our real-time fMRI system returns a neurofeedback generated by FCMA under 1 second, and updates the voxel selection and the training model in 3 seconds using 48 compute nodes, which meet the real-time fMRI study requirements. We have used the FCMA toolbox to conduct an initial neuroscience study to show the effectiveness of FCMA. When applying FCMA to a simple fMRI dataset in which subjects viewed blocks of faces or scenes, FCMA identifies category-selective brain regions in terms of correlation in medial prefrontal cortex which cannot be found using traditional activity-based analysis approaches.