[talks] Y Wang general exam

[Melissa Lawson]

Yida Wang will present his research seminar/general exam on Thursday May 19 
at 2PM in Room 302 (note room!).  The members of his committee are;  Kai 
Li (advisor), Moses Charikar, Rob Schapire.  Everyone is invited to attend his talk 
and those faculty wishing to remain for the oral exam following are welcome to 
do so.  His abstract and reading list follow below.

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Full Correlation Computation and Analysis of Large-Scale FMRI

Abstract:
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. In this project, we are conducting our research by 
fully leveraging recent advances in large-scale computing techniques, 
for the first time, to analyze the full correlation matrix of fMRI data. 
The success of our approach will result in a quantum increase in the 
power of neuroimaging analysis, extracting 7 orders of magnitude more 
information from the data than previous approaches and opening up 
qualitatively new opportunities for neuroscience research. In order to 
fully leverage the advances in multicore processors and parallel 
computing, we have developed a parallel data analysis tool for a cluster 
of computers. This tool can achieve high utilization of CPU cores on a 
cluster. On a system with 528 CPU cores, the tool is able to perform 
full-correlation study of one-hour fMRI dataset in 2.5 days, more than 
an order of magnitude improvement over a native parallel approach. We 
have also studied how to analyze and visualize massive amounts of 
full-correlation result data (petabytes) and built corresponding tools 
for the data analysis pipeline. In this talk, I will describe our 
approach and report the current status and future plans.

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:
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.

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).

R. J. Bayardo, Y. Ma and R. Srikant. Scaling up all pairs similarity 
search. In WWW, 2007.

R. Vernica, M. J. Carey and C. Li. Efficient Parallel Set-Similarity 
Joins Using MapReduce. In SIGMOD, 2010.

J. Cheverud and G. Marriog. Comparing covariance matrices: Random 
skewers method compared to the common principal components model. 
Genetics and Molecular Biology, 2007.