My research focuses on heterogeneity characterization of subsurface media and quantification of the level and consequence of the associated uncertainty. A detailed subsurface characterization is crucial for accurate prediction of the dynamics of subsurface fluids such as water, contaminants, CO2, and brine, and a successful characterization relies on the effective use of measurement data from multiple physical processes as well as the use of advanced computing techniques and technologies such as large-scale optimization and High Performance Computing (HPC). When the characterization results are fed to management models such as a remediation optimization model or a CO2 injection optimization model, I am interested in not only the level of uncertainties (i.e., variance or higher moments) associated with the characterization, but the consequence of uncertainties for the management model. For the same characterization, the consequence varies with the management model; hence a context-specific measure of uncertainty is needed. However, traditional measures such as statistical moments are not context-specific and not suitable for this purpose.