In this talk, we study the multiscale Boltzmann equation with multi-dimensional random parameters by a bi-fidelity stochastic collocation (SC) method. By carefully choosing the compressible Euler equation as the low-fidelity model, we adapt the bi-fidelity SC method to combine computational efficiency of the low-fidelity model with high accuracy of the high-fidelity (Boltzmann) model, at a significantly reduced simulation cost. With only a small number of high-fidelity asymptotic-preserving solver runs for the Boltzmann equation, the bi-fidelity approximation can capture the macroscopic quantities dependent on the solution to the Boltzmann equation in the random space. A priori estimate on the accuracy between the high-fidelity and bi-fidelity solutions together with a convergence rate analysis is established. Similarly, we adapt the same SC method to study the linear transport equation under the diffusive scaling and by choosing the Goldstein-Taylor equation as the low-fidelity model. Extensive numerical experiments are presented to verify the efficiency and accuracy of our proposed methods. This is a joint work with Xueyu Zhu.