In this talk, I will discuss multiscale methods and construction of efficient hierarchical model reduction tools. The proposed approaches are shown to converge independent of small scales and contrast. I will discuss the applications of these approaches in uncertainty quantification. In particular, I will discuss multilevel Markov chain Monte Carlo methods within a general framework of Bayesian inversion.