Special Meeting: Workshop on Algorithms for Modern, Massive Datasets

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This special meeting will bring together people from the ComputationalStatistics and Numerical Linear Algebra communities with people from theTheoretical Computer Science community to promote cross-fertilization ofideas. This will hopefully lead to a better understanding of modernmassive data sets as well as invention of new techniques and algorithmsfor their modeling and analysis. Most conferences and workshops oninformation retrieval and data mining are dominated by studies oftraditional data sets and traditional algorithmic techniques.Nevertheless, modern data sets have become extremely complex and haverendered traditional approaches for processing them ineffective in manyinstances. The difficulties occur primarily in two ways: modern data setsare often high-dimensional and massive, and thus novel dimensionalityreduction or sampling-based techniques have to be employed in processingthem, and modern data sets often have complicated nonlinear structures,and must be modeled with appropriate mathematical objects such as tensors,symmetric spaces, Lie groups, etc. This special meeting will study datasets with such complex structures and explore novel techniques foranalyzing such data.The techniques that will be discussed at the meeting have applicability innumerous fields of practical and important interest. For example:(1) Applications in Bioinfomatics and the Medical Sciences includeimproved methods to analyze large microarray and high-throughput chemicaldata sets for developing new medications, identifying novel gene products,elucidating protein folding pathways, and the improved detection andclassification of cancer.(2) Applications in the Social Sciences include improved methods to modelthe combinatorial structure of large social networks; this has been usedand will continue to be used to, e.g., identify potential terrorist cellshidden in a communications network.(3) Applications in the Geosciences include remote sensors in air-andspacecraft that produce more data than can currently be stored, but whichmust be analyzed for environmental planning, weather forecasting, andpublic health contamination issues.(4) Applications in Computer Vision include novel ways to identify noisyimages of targets and faces for improved target identification andindividual identification in more realistic settings.