Representations that mix graphical models and first-order logic—called either first-order or relational probabilistic models—are becoming increasingly popular. In these models, random variables are parameterized by logical variables. As exact inference for first-order probabilistic graphical models at the propositional level can be formidably expensive, there is an ongoing effort to design efficient lifted inference algorithms for such models. The idea behind lifted inference is to carry out as much inference as possible without propositionalizing. This talk focuses on directed first-order models that require an aggregation operator when a parent random variable is parameterized by logical variables that are not present in a child random variable. I will describe our work on extending Milch et al.'s C-FOVE lifted inference algorithm with efficient lifted aggregation procedures.