Computational Statistics and Simulation

Research in the Computational Statistics and Simulation group involves modeling, analyzing, and simulating dynamic systems characterized by complex process logic and uncertain behaviors. Methodological interests in these areas include data-mining, response-surface, time-series, and simulation-optimization methods; spatial-temporal data analysis and pattern recognition; and Monte Carlo and discrete event simulation. Technological components concern the integration of decision and information sciences to address the representation, storage, dissemination, processing, analysis, and interpretation of large data sets. These tools are applied in a variety of areas, such as computational finance, environmental monitoring, object/target recognition, statistical signal and image processing, crime analysis, health care delivery, logistics and distribution, manufacturing, and aerospace systems.