Robust aerodynamic shape optimization of aircraft components is a computationally expensive process due to a high number of parametrized design variables of complex geometry definitions especially when uncertainties are involved. Therefore, reducing the number of design variables and complexity has vital importance to respond over a range of on-design requirements with the need to meet design constraints in the robust optimization process. In this study, the Proper Orthogonal Decomposition (POD) method is incorporated into the Inductive Design Exploration Method (IDEM) for the aerodynamic shape optimization process of an aircraft wing with reduced computational effort. POD data reduction technique is utilized to filter design variables of the wing by extracting the dominant features of the system to reduce the number of design variables in the robust optimization process. POD-based Radial Basis Function (RBF) data estimation surrogate model is also implemented to enrich design alternatives for the optimization framework with a reduced computational cost. Model uncertainties due to the Computational Fluid Dynamics (CFD) turbulence model are introduced to IDEM as epistemic uncertainties using the eigenspace perturbation methodology. The results of the case study show that the proposed IDEM can obtain robust wing designs satisfying the performance goals while accounting for the uncertainties in the top-to-bottom approach.