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Jason Eisner is Professor of Computer Science at Johns Hopkins University, as well as Director of Research at Microsoft Semantic Machines. He is a Fellow of the Association for Computational Linguistics. At Johns Hopkins, he is also affiliated with the Center for Language and Speech Processing, the Mathematical Institute for Data Science, and the Cognitive Science Department. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure. His 170+ papers have 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 declarative programming language that provides an infrastructure for AI algorithms. He has received two school-wide awards for excellence in teaching, as well as recent Best Paper Awards at ACL 2017, EMNLP 2019, and NAACL 2021 and an Outstanding Paper Award at ACL 2022.
Jason was born in Ithaca, New York and grew up in suburban New Jersey. His undergraduate major at Harvard University (summa cum laude, junior-year election to Phi Beta Kappa) was in the Cognitive Science track of the Psychology Department.
After a year in South Africa during its political transition, on a Fulbright Scholarship in Creative Writing, he spent two years at Cambridge University on a Herchel C. Smith Scholarship, where he earned a second undergraduate degree, this time in Mathematics (first-class honors).
His Ph.D. in Computer Science (on an NSF fellowship) was at the University of Pennsylvania, under Mitch Marcus. He also spent a good deal of time in Penn's Linguistics Department. He joined the University of Rochester as an assistant professor, then moved to Johns Hopkins University (JHU) soon afterward.
At JHU, he is a full professor of Computer Science, with a joint appointment in Cognitive Science. He is also a core member of the Center for Language and Speech Processing. He was the recipient of an NSF CAREER Award as well as other funding from NSF and DoD. He has twice received school-wide awards for excellence in teaching, as well as recent Best Paper Awards at ACL 2017, EMNLP 2019, and NAACL 2021.
Jason has authored over 170 papers (as well as software tools) in several areas of computational linguistics, especially parsing, grammar induction, machine translation, computational phonology, computational morphology, and weighted finite-state methods. He is the lead designer of the Dyna programming language, the Dopp programming language parser, and the Dynasty hypergraph browser. His voice can be heard as the top hit for the admittedly rare query parsing song, he served as a judge on NLP Idol, and he's had some other work-related fun, such as turning down a Google interview back in 1999 and investing in Lernout & Hauspie a week before they crashed in a scandal. His hobbies include playing violin and squash (not at the same time), singing show tunes to his kids Talia and Lev, biking to work, reading, rock climbing with his wife Debbie, and occasionally performing in musical theater.
Jason has also contributed much to the NLP community's conference and publication practices, as well as its diversity and inclusion initiatives. He has served in various roles such as program chair, diversity & inclusion co-chair (first ever), area chair, workshop chair, journal action editor, SIGMORPHON president, NACLO problem committee member, invited speaker, and general kibitzer.
You can browse Jason's technical work at http://cs.jhu.edu/~jason/papers. His many professional activities are listed on his full CV.
At a recent keynote talk, my close colleague Sanjeev Khudanpur poured on the syrup with this flattering (and amusing) introduction of me: 4-minute video. Nice guy!