MSPA-MCS: Small-sample Network Inference in Computational Vision and Biology

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Prop ID: CCF-0625687 PI: Geman, Donald J. Institution: Johns Hopkins University Title: MSPA-MCS: Small-sample Network Inference in Computational Vision and Biology AbstractThe investigators will develop theoretical models and statistical procedures which are designed to address the ``small-sample dilemma'' in the computational sciences. They will concentrate on problems arising at the interface of applied mathematics and computer science with both molecular biology (bioinformatics) and with image analysis (computer vision). The small-sample dilemma refers to the disparity between the high dimensionality of the raw data, for example the large number of variables necessary to convey the states of genes and proteins or to encode the content of digital images, and the relatively small number of actual samples (repetitions, replications, etc.) available for obtaining useful information. In bioinformatics, the investigators will work on modeling cellular pathways and classifying phenotypes, including disease, according to molecular signatures. The challenge is to uncover a complex network of molecular interactions from a relatively small number of training examples. In computer vision, the investigators will study patterns of spatial interactions among image features carrying visual cues