Random projections are random matrices that can be used to perform dimensionality reduction on vectors. We show how they can be applied to decrease the number of variables and/or constraints of some Mathematical Programming classes, notably Linear Programming, Conic Programming, Quadratic Programming, Quadratically Constrained Quadratic Programming -- and even a convex MINLP encoding a famous clustering problem, and comment on their practical usefulness.