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Data science components such as statistics, data mining, predictive analytics, and machine learning are being applied to improve urban transportation services, customer experiences, diagnostics, and financial decision making. CSL researchers and their partners are addressing massive challenges in collecting data, distributing data to appropriate storage facilities over distributed networked systems, and processing such data on high-performance computers and/or clouds.

For example, some are working on solutions that will help analysts compare, organize, and compress opinion data easily and quickly. Others are looking to speed up and improve our ability to collect and analyze data and subsequently adapt our decisions as new information comes in. Looking even further ahead, CSL researchers are leading the formulation of approaches for interweaving and correlating data from different sources to gain valuable knowledge about their interdependencies and trade-offs that need to be evaluated. The results of their work could be applied to food-energy-water nexus decision-making to balance water availability, energy generation, and food production. CSL researchers, who recognize that data analytics are quickly moving from being limited by data sample size to being limited by processing power and network capacity, are leading the development of fundamental theory based on this paradigm.