The research act ivities in the CASS lab primarily focus on the development of a computationally tractable dynamic data driven framework to address challenges associated with accurate modeling, forecasting and control of engineering systems under uncertainty. These research challenges include developing non-parametric models from data, characterizing errors associated with models, propagating non-Gaussian uncertainties for large scale nonlinear systems, assimilating high dimensional noisy data with forecast model states, and incorporating the next generation of mobile sensors (such as drones) as big data collection and processing units. Hence, the research work at CASS lab is an amalgamation of fundamental concepts in modeling, sensing, uncertainty analysis, optimal control theory, information theory and high performance computing.