Abstract: In this talk, we explore cutting-edge advancements in computational statistics and machine learning to tackle key challenges in contemporary materials science. At the core of this discussion is the grand challenge of multi-scale, multi-physics materials design, with far-reaching implications for industries such as manufacturing, aerospace, defense, and energy. We will cover recent developments in multi-fidelity and forward uncertainty quantification using polynomial chaos expansion, Bayesian optimization leveraging various Gaussian process regression methods, and stochastic modeling for microstructure evolution. Additionally, we will discuss reduced-order modeling for crystal plasticity finite element methods, generative models for large-scale microstructure reconstruction, and applications in additive manufacturing. The talk will conclude with a forward-looking perspective on multi-scale materials digital twins and their role in future advancements.