Scientists and engineers use computer simulations to study relationships between a physical model's input parameters and its outputs.  However, thorough parameter studies---e.g., constructing response surfaces, optimizing, or averaging---are challenging, if not impossible, when the simulation is expensive and the model has several inputs.  To enable studies in these instances, the engineer may attempt to reduce the dimension of the model's input parameter space.  Active subspaces are a set of dimension reduction tools that identify important directions in the parameter space.  I will describe methods for discovering a model's active subspace and propose strategies for exploiting the reduced dimension to enable otherwise infeasible parameter studies. For more info, see www.activesubspaces.org