Jason Eisner

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Jason Eisner (opens in new tab) is Director of Research at Microsoft Semantic Machines, as well as Professor of Computer Science at Johns Hopkins University. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure. His 150+ papers have presented various algorithms for parsing, machine translation, 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. He has received recent Best Paper Awards at NAACL 2021, EMNLP 2019, and ACL 2017, as well as two school-wide awards for excellence in teaching.