World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
7472
World Ranking
8387
National Ranking
3593

Overview

Haym Hirsh is affiliated with Cornell University in the United States. Their research contributions include work published in venues such as Nature Machine Intelligence.

The scientist has worked on topics related to accelerating evidence-informed decision-making for global development goals, with specific focus on applications of machine learning. A recent publication titled Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning was published in 2020 in Nature Machine Intelligence.

Collaborative efforts are notable in their work, with frequent co-authors including Jaron Porciello, Maryia Ivanina, Maidul Islam, and Stefan Einarson.

Publication venues associated with their work include:

  • Nature Machine Intelligence

Recent papers documented include:

  • Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning, 2020, Nature Machine Intelligence

Best Publications

  • Recommendation as classification: using social and content-based information in recommendation

    Chumki Basu;Haym Hirsh;William Cohen

  • Learning to predict rare events in event sequences

    Gary M. Weiss;Haym Hirsh

  • Predicting Sequences of User Actions

    Brian D. Davison;Haym Hirsh

  • Computing least common subsumers in description logics

    William W. Cohen;Alex Borgida;Haym Hirsh

  • Learning the classic description logic: theoretical and experimental results

    William W. Cohen;Haym Hirsh

  • Mining Text Using Keyword Distributions

    Ronen Feldman;Ido Dagan;Haym Hirsh

  • Joins that generalize: text classification using WHIRL

    William W. Cohen;Haym Hirsh

  • Amplify scientific discovery with artificial intelligence

    Yolanda Gil;Mark Greaves;James Hendler;Haym Hirsh

  • Using LSI for text classification in the presence of background text

    Sarah Zelikovitz;Haym Hirsh

  • Mining associations in text in the presence of background knowledge

    Ronen Feldman;Haym Hirsh

  • Technical paper recommendation: a study in combining multiple information sources

    Chumki Basu;Haym Hirsh;William W. Cohen;Craig Nevill-Manning

  • Towards Measuring Similarity in Description Logics.

    Alexander Borgida;Thomas J. Walsh;Haym Hirsh

  • Learning to personalize

    Haym Hirsh;Chumki Basu;Brian D. Davison

  • Knowledge Management: A Text Mining Approach

    R. Feldman;M. Fresko;H. Hirsh;Y. Aumann

  • Generalizing Version Spaces

    Haym Hirsh

  • A Quantitative Study of Small Disjuncts

    Gary M. Weiss;Haym Hirsh

  • Gado: a genetic algorithm for continuous design optimization

    Khaled Mohamed Rasheed;Haym Hirsh

  • Exploiting Background Information in Knowledge Discovery from Text

    Ronen Feldman;Haym Hirsh

  • A genetic algorithm for continuous design space search

    Khaled Rasheed;Haym Hirsh;Andrew Gelsey

  • The Learnability of Description Logics with Equality Constraints

    William W. Cohen;Haym Hirsh

  • Enabling technologies: learning to personalize.

    Haym Hirsh;Chumki Basu;Brian D. Davison

Frequent Co-Authors

William W. Cohen
William W. Cohen Carnegie Mellon University
Brian D. Davison
Brian D. Davison Lehigh University
Gary M. Weiss
Gary M. Weiss Fordham University
Cynthia Rudin
Cynthia Rudin Duke University
Ronen Feldman
Ronen Feldman Hebrew University of Jerusalem
Yolanda Gil
Yolanda Gil University of Southern California
Foster Provost
Foster Provost New York University
Ido Dagan
Ido Dagan Bar-Ilan University
Alexander Borgida
Alexander Borgida Rutgers, The State University of New Jersey
Leonard Pitt
Leonard Pitt University of Illinois at Urbana-Champaign

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