Jason Eisner
Jason Eisner develops models and algorithms for linguistic structure, including parsing, translation, and conversational AI. Eisner studies probabilistic modelling, inference, and learning for linguistic structure. His work includes parsing, machine translation, weighted finite-state methods, and learning syntax and morphology with limited supervision. Eisner served as director of research at Microsoft Semantic Machines from 2019 to 2024. The group developed approaches to conversational AI.
- Now at
- Johns Hopkins University
- Works
- 223
- Citations
- 7,895
- h-index
- 51
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
-
2018
Research recognition
ACL elected Jason Eisner as a Fellow in 2018.
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2019–2024
Conversational AI research
Eisner served as director of research at Microsoft Semantic Machines from 2019 to 2024. The group developed approaches to conversational AI.
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As of October 2026
Models of language structure
Eisner studies probabilistic modelling, inference, and learning for linguistic structure. His work includes parsing, machine translation, weighted finite-state methods, and learning syntax and morphology with limited supervision.
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As of October 2026
Dyna programming language
Eisner is the lead designer of Dyna, a declarative programming language intended to provide infrastructure for AI algorithms.
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As of October 2026
Language and reasoning
His research goals include connecting language models to commonsense reasoning, formal reasoning, and downstream applications.
Sources and credits
Photo: cs.jhu.edu; Jason Eisner - Bio; creator not stated. License: Not stated by source. Source: cs.jhu.edu. Cropped to a square and resized. Ask us to remove or replace this photo.