Abstract: Material properties are governed by both chemical composition and microstructure. With advancements in additive manufacturing, we can now engineer metamaterials that tune properties through microstructural design, without altering composition or fabrication parameters. By assembling metamaterial microstructures aperiodically, we create heterogeneous metamaterial systems (HMS) capable of accommodating spatially varying property requirements. Despite their potential, designing HMS remains challenging due to the high-dimensional design space, complex structure-property relationships, mixed-type design variables, and multiscale interactions. In this talk, we will introduce data-driven frameworks that accelerate the multiscale design of HMS. We will cover database generation, surrogate modeling, generative unit-cell design, and multiscale optimization, all centered around latent variable representations. We will present the development of various latent variable models, such as latent variable Gaussian processes and variational autoencoders, to learn low-dimensional, interpretable representations of complex microstructures, enabling effective cross-scale property modeling and efficient inverse design. By integrating these models into multiscale topology optimization, we develop data-driven frameworks that simultaneously design macro- and microstructures in HMS, enabling precise control over spatial property distributions. They provide unprecedented design flexibility and efficiency, enabling applications in deformation and vibration control, fracture resistance, and strain cloaking.