Over past decade,reduced-order models have been found increased use to greatly reduce the computational cost in the areas such as flow control and optimization.In this lecture,we will present an effective stochastic reduced-order modeling method that combines the advantages of proper orthogonal decomposition and centroidal Voronoi tessellations.The optimality of such hybrid method for model reduction is discussed and numerical tests are performed to validate our results.