Jordan's primary research revolves around computational approaches to child language acquisition and their broader implications. In particular, algorithmic models of grammar acquisition, especially morphology, how those processes drive language variation and change, what insights they provide for low-resource NLP, and what they tell us about the intersection of NLP and cognitive science. His work on NLP has included morphological and syntactic evaluation, models of morphological segmentation and inflection, and transliteration. His research on acquisition and change has touched on several varieties of English as well as Arabic, Armenian, German, Korean, and Spanish among others, while his work in low-resource NLP aims to include a wide range of world languages, both well-studied and under-served.