World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
22894
World Ranking
2539
National Ranking
1267

Overview

Jason Eisner is affiliated with Johns Hopkins University in the United States. Their research primarily addresses topics within computer science, with a focus on artificial intelligence.

Their publication record includes 83 works in computer science, of which 72 fall under artificial intelligence. Additional subfields covered include computer vision and pattern recognition, computational theory and mathematics, geometry and topology, and information systems.

Eisner's main research topics involve:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and Dialogue Systems
  • Multimodal Machine Learning Applications
  • Machine Learning and Algorithms
  • Logic, Reasoning, and Knowledge
  • Logic, Programming, and Type Systems

Frequent coauthors include Benjamin Van Durme, Sam Thomson, Adam Pauls, Ryan Cotterell, and Tim Vieira, reflecting collaborative work across multiple projects.

The scientist has contributed to several publication venues, with a strong presence on arXiv (Cornell University) featuring 27 publications. Other venues include:

  • Repository for Publications and Research Data (ETH Zurich)
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent papers by Jason Eisner include:

  • Constrained Language Models Yield Few-Shot Semantic Parsers, 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Decision-Oriented Dialogue for Human-AI Collaboration, 2024, Transactions of the Association for Computational Linguistics
  • Learning How to Ask: Querying LMs with Mixtures of Soft Prompts, 2021, arXiv (Cornell University)
  • Noise-Contrastive Estimation for Multivariate Point Processes, 2020, arXiv (Cornell University)
  • Transformer Embeddings of Irregularly Spaced Events and Their Participants, 2021, arXiv (Cornell University)

Best Publications

  • Secure data interchange

    Frederick S. M. Herz;Walter Paul Labys;David C. Parkes;Sampath Kannan

  • System for the automatic determination of customized prices and promotions

    Frederick Herz;Jason Eisner;Lyle Unger;Walter Paul Labys

  • Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

    Adithya Renduchintala;Rebecca Knowles;Philipp Koehn;Jason Eisner

  • Three new probabilistic models for dependency parsing: an exploration

    Jason M. Eisner

  • The neural hawkes process: a neurally self-modulating multivariate point process

    Hongyuan Mei;Jason Eisner

  • Contrastive Estimation: Training Log-Linear Models on Unlabeled Data

    Noah A. Smith;Jason Eisner

  • Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

    Guanghui Qin;Jason Eisner

  • Learning Non-Isomorphic Tree Mappings for Machine Translation

    Jason Eisner

  • Using ``Annotator Rationales'' to Improve Machine Learning for Text Categorization

    Omar Zaidan;Jason Eisner;Christine Piatko

  • The SIGMORPHON 2016 Shared Task - Morphological Reinflection.

    Ryan Cotterell;Christo Kirov;John Sylak-Glassman;David Yarowsky

  • Parameter Estimation for Probabilistic Finite-State Transducers

    Jason Eisner

  • Efficient Generation in Primitive Optimality Theory

    Jason Eisner

  • Efficient Parsing for Bilexical Context-Free Grammars and Head Automaton Grammars

    Jason Eisner;Giorgio Satta

  • Bilexical Grammars and their Cubic-Time Parsing Algorithms

    Jason Eisner

  • Learning to Search in Branch and Bound Algorithms

    He He;Hal Daume Iii;Jason M Eisner

  • Lempel-ziv-datenkompressionsverfahren unter verwendung eines wörterbuchs mit häufig auftretenden buchstabenkombinationen, wörtern und/oder sätzen

    Jeffrey C Reynar;Frederick Herz;Jason M Eisner;Lyle Ungar

  • Dependency Parsing by Belief Propagation

    David Smith;Jason Eisner

  • Directional constraint evaluation in Optimality Theory

    Jason Eisner

  • Minimum Risk Annealing for Training Log-Linear Models

    David A. Smith;Jason Eisner

  • Modeling Annotators: A Generative Approach to Learning from Annotator Rationales

    Omar Zaidan;Jason Eisner

  • CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages

    Ryan Cotterell;Christo Kirov;John Sylak-Glassman;Géraldine Walther

Frequent Co-Authors

Ryan Cotterell
Ryan Cotterell ETH Zurich
Noah A. Smith
Noah A. Smith University of Washington
David Yarowsky
David Yarowsky Johns Hopkins University
Philipp Koehn
Philipp Koehn Johns Hopkins University
Mark Dredze
Mark Dredze Johns Hopkins University
Sanjeev Khudanpur
Sanjeev Khudanpur Johns Hopkins University
Lyle H. Ungar
Lyle H. Ungar University of Pennsylvania
Hal Daumé
Hal Daumé University of Maryland, College Park
Daniel Klein
Daniel Klein University of California, Berkeley
Benjamin Van Durme
Benjamin Van Durme Johns Hopkins University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring further study or career paths in computing and technology can be both rewarding and flexible, thanks to the growing variety of online degree programs. For those looking to enhance their business acumen alongside computer science skills, cheapest online mba programs offer a cost-effective way to earn a valuable credential. These degrees can open leadership opportunities in tech-driven fields.

If time is a priority, consider a 1 year masters degree to fast-track your advancement. These accelerated programs are ideal for professionals wanting to quickly gain new expertise.

Alternatively, those seeking quick entry into the workforce might explore easiest online degrees that pay well. These programs are designed for efficiency and can lead to well-compensated roles in technology and related industries.

For those fascinated by cutting-edge tech, online degrees in ai provide specialized training in one of today’s most sought-after fields, offering strong career prospects in both the US and globally.

Best Scientists Citing Jason Eisner

Trending Scientists