The least squares estimator in shape constrained regression problems such as isotonic and convex regression automatically adapts to some natural underlying sparsity. I will talk about these results and, in the process, describe some aspects of a general theory for the estimation of a normal mean under convexity constraints. I will also explain connections to the classical random...