I'll highlight recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given an n x d matrix A, one first compresses A to an m x d matrix S*A, where S is a certain m x n random matrix with m much less than n. Much of the expensive computation is then performed on S*A, thereby accelerating the solution for the original problem involving A. I'll discuss recent advances in least squares as well as robust regression, including least absolute deviation and M-estimators. I'll also discuss low rank approximation, and a number of variants of these problems. Finally, I'll mention limitations of the method.