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Keith Noto is a postdoc in the Computer Science department, working on anomaly detection in human fetal gene expression data. That is, how does one distinguish "normal" development (meaning: like what we've seen before) from "abnormal" (different from what we've seen before, in the right way) over hundreds of samples with tens of thousands of molecular measurements each, when they don't even really know what they're looking for? They use the Tufts TTS cluster to test our approaches to this problem on dozens of separate data sets. These computational experiments take thousands of CPU hours, so their work cannot be done on just a handful of machines.