World's Best Scientists 2026 revealed!
Ryan McDonald

Ryan McDonald

D-Index & Metrics

Computer Science

D-Index
65
Citations
23010
World Ranking
2415
National Ranking
1208

Overview

Ryan McDonald is affiliated with ASAPP in the United States and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence. Their research covers several subfields including molecular biology, infectious diseases, cognitive neuroscience, and signal processing.

Their main research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and Dialogue Systems
  • Advanced Text Analysis Techniques
  • Biomedical Text Mining and Ontologies
  • Text Readability and Simplification
  • SARS-CoV-2 and COVID-19 Research

Ryan McDonald's publication record features papers in both peer-reviewed conferences and preprint servers. Significant recent papers include:

  • "Universal Dependencies" (2025), published in Elsevier eBooks
  • "Planning with Learned Entity Prompts for Abstractive Summarization" (2021), published in Transactions of the Association for Computational Linguistics
  • "Universal Dependencies" (2025), published in HAL (Le Centre pour la Communication Scientifique Directe)
  • "Decoding Part-of-Speech from Human EEG Signals" (2022), published in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble" (2020), published in arXiv (Cornell University)

Frequently publishing in venues such as arXiv (Cornell University), Elsevier eBooks, Transactions of the Association for Computational Linguistics, and proceedings of the Annual Meeting of the Association for Computational Linguistics, McDonald maintains an active profile in both open-access platforms and established scientific outlets.

Collaborations form an important part of their work, with frequent co-authors including:

  • Shashi Narayan
  • Kilian Q. Weinberger
  • Jennifer Foster
  • Filip Ginter
  • Jan Hajič

The mix of topics and venues demonstrates a diverse engagement with both applied and theoretical aspects of computational linguistics and allied disciplines. Their work in biomedical text mining and COVID-19 research situates them at the intersection of computer science and healthcare-related data analysis.

Best Publications

  • Domain Adaptation with Structural Correspondence Learning

    John Blitzer;Ryan McDonald;Fernando Pereira

  • Universal Dependencies v1: A Multilingual Treebank Collection

    Joakim Nivre;Marie-Catherine de Marneffe;Filip Ginter;Yoav Goldberg

  • Non-Projective Dependency Parsing using Spanning Tree Algorithms

    Ryan McDonald;Fernando Pereira;Kiril Ribarov;Jan Hajic

  • A Universal Part-of-Speech Tagset

    Slav Petrov;Dipanjan Das;Ryan McDonald

  • Modeling online reviews with multi-grain topic models

    Ivan Titov;Ryan McDonald

  • Online Large-Margin Training of Dependency Parsers

    Ryan McDonald;Koby Crammer;Fernando Pereira

  • The CoNLL 2007 Shared Task on Dependency Parsing

    Joakim Nivre;Johan Hall;Sandra K"ubler;Ryan McDonald

  • A Joint Model of Text and Aspect Ratings for Sentiment Summarization

    Ivan Titov;Ryan McDonald

  • On Faithfulness and Factuality in Abstractive Summarization

    Joshua Maynez;Shashi Narayan;Bernd Bohnet;Ryan Thomas Mcdonald

  • Dependency Parsing

    Sandra Kubler;Ryan McDonald;Joakim Nivre;Graeme Hirst

  • Universal Dependency Annotation for Multilingual Parsing

    Ryan McDonald;Joakim Nivre;Yvonne Quirmbach-Brundage;Yoav Goldberg

  • Online Learning of Approximate Dependency Parsing Algorithms.

    Ryan T. McDonald;Fernando C. N. Pereira

  • Universal Dependencies 2.2

    Joakim Nivre;Mitchell Abrams;Željko Agić;Lars Ahrenberg

  • Building a Sentiment Summarizer for Local Service Reviews

    Sasha Blair-Goldensohn;Kerry Hannan;Ryan McDonald;Tyler Neylon

  • A study of global inference algorithms in multi-document summarization

    Ryan McDonald

  • Structured Models for Fine-to-Coarse Sentiment Analysis

    Ryan McDonald;Kerry Hannan;Tyler Neylon;Mike Wells

  • Characterizing the Errors of Data-Driven Dependency Parsers

    Ryan McDonald;Joakim Nivre

  • Multilingual Dependency Analysis with a Two-Stage Discriminative Parser

    Ryan McDonald;Kevin Lerman;Fernando Pereira

  • Distributed Training Strategies for the Structured Perceptron

    Ryan McDonald;Keith Hall;Gideon Mann

  • Multi-Source Transfer of Delexicalized Dependency Parsers

    Ryan McDonald;Slav Petrov;Keith Hall

  • Universal Dependencies 2.7

    Daniel Zeman;Joakim Nivre;Mitchell Abrams;Elia Ackermann

Frequent Co-Authors

Joakim Nivre
Joakim Nivre Uppsala University
Slav Petrov
Slav Petrov Google (United States)
Fernando Pereira
Fernando Pereira Google (United States)
Yoav Goldberg
Yoav Goldberg Bar-Ilan University
Jan Hajič
Jan Hajič Charles University
Dipanjan Das
Dipanjan Das Google (United States)
Christopher D. Manning
Christopher D. Manning Stanford University
Sampo Pyysalo
Sampo Pyysalo University of Turku
Filip Ginter
Filip Ginter University of Turku
Marie-Catherine de Marneffe
Marie-Catherine de Marneffe The Ohio State University

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