Yida Wang will present his research seminar/general exam on Friday January 13 at 9:30AM in Room 302 (note room). The members of his committee are: Kai Li (advisor), Moses Charikar, and Nicholas Turk-Browne (Psychology, Neuroscience) . Everyone is invited to attend his talk, and those faculty members wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. ------------------------------- Computing and decoding brain full pairwise correlation matrices Human brain imaging such as Functional Magnetic Resonance Imaging (fMRI) has made transformational impacts on neuroscience. However, due to technology limitations in the past, researchers have constrained their analyses by making assumptions that are biased or false, leading to missed opportunities for science discovery, and in the worst case, incorrect inferences.Realizing that the processes in the brain may depend on widely distributed interactions among regions, we propose a novel method of analyzing the brain by its full correlation structure and show the effectiveness of the correlation analysis. The full correlation matrices lead to dramatic increase of data amount by at most extracting 7 orders of magnitude more information. With careful data computing, allocating and appropriate feature selection, we design a parallel tool to get the full correlation matrices in distribution and analyze them efficiently via classification to show the effectiveness of connectivity analysis. The critical voxels picked by our tool well match the existing theory of cognitive neuroscience in terms of location. And based on the selected voxels, experimental results demonstrate that the correlation based classification gets a comparable accuracy as traditional activation based classification within a subject, and is able to attain substantially higher accuracy than activation when analyzing across subjects, which, for the first time, shows the stability of brain correlation structures among different subjects. This work is advised by Prof. Moses Charikar and Prof. Kai Li at Princeton Computer Science Department, and Prof. Nicholas Turk-Browne and Prof. Jonathan Cohen at Princeton Neuroscience Institute. Reading List: Book: Modern Operating Systems, 3rd Edition, Andrew Tanenbaum. Computer Architecture: A Quantitative Approach, 4th Edition, John Hennessy and David Patterson. Paper: Goto, K. and Van De Geijn, R. 2008. Anatomy of high-performance matrix multiplication. ACM Trans. Math. Softw. 34, 3. J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters, Proc. 6th Symp. Operating System Design and Implementation (OSDI), Usenix Assoc., 2004, pp. 137-150. Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael Franklin, Scott Shenker, and Ion Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. Technical Report UCB/EECS-2011-82, EECS Department, University of California, Berkeley, Jul 2011. Pereira F, Mitchell T M and Botvinick M. Machine learning classifiers and fMRI: A tutorial overview Neuroimage. 2008 Norman, K. A., Polyn, S. M., Detre, G. J. and Haxby, J. V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424-430 (2006). Jacob Y, Rapson A, Kafri M, Baruchi I, Hendler T, Jacob EB. Revealing voxel correlation cliques by functional holography analysis of fMRI. Journal of Neuroscience Methods 2010; 191: 126-137. Smith SM. Overview of fMRI analysis. The British Journal of Radiology, 77 (2004), 167-175. Kriegeskorte N, Goebel R, Bandettini P. Information-based functional brain mapping. Proceedings of the National Academy of Sciences 2006; 103(10): 3863-3868. Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR, Gobbini MI, Hanke M, Ramadge PJ. A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72(2), 2011.