[talks] Y Wang general exam
mml at CS.Princeton.EDU
Fri May 13 16:42:31 EDT 2011
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.
Full Correlation Computation and Analysis of Large-Scale FMRI
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.
Computer Architecture: A Quantitative Approach, 4th Edition, John
Hennessy and David Patterson
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.
More information about the talks