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Coarse-graining reduces complexity of atomic models while preserving essential physical behavior. When done systematically, it enables efficient and predictive modeling of large systems. Our lab develops coarse-graining approaches grounded in statistical physics rather than ad hoc simplifications. These methods allow us to study macromolecular assemblies at scales relevant to real materials. We emphasize interpretability and transferability in our models. This expertise makes it possible to tackle problems that are otherwise computationally intractable.