Andrew Pangborn

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Like many modern techniques for scientific analysis, flow cytometry produces massive amounts of data that must be analyzed and clustered intelligently to be useful. Current manual binning techniques are cumbersome and limited in both the quality and quantity of analysis produced. To address the quality of results, a new framework applying two different sets(More)
The computational demands of multivariate clustering grow rapidly, and therefore processing large data sets, like those found in flow cytometry data, is very time consuming on a single CPU. Fortunately these techniques lend themselves naturally to large scale parallel processing. To address the computational demands, graphics processing units, specifically(More)
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