A huge portion of human communication, thought, and culture now passes through computers, thus computers must learn to understand human language. Ultimately, we want our devices to help us by understanding text and speech as a human would—both at the small scale of intelligent user interfaces and at the large scale of the entire multilingual Internet. Human language is fascinatingly complex and ambiguous. Yet babies are born with the incredible ability to discover the structure of the language around them. Soon they are able to rapidly comprehend and produce that language and relate it to events and concepts in the world. Figuring out how this is possible is a grand challenge for both cognitive science and machine learning. Dr. Eisner’s goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure. He has presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; and unsupervised or semi-supervised learning methods for syntax, morphology, and word-sense disambiguation. He is also the lead designer of Dyna, a new declarative programming language that provides an infrastructure for AI research.