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Researcher · University of Maryland, College Park · United States

Jordan Lee Boyd-Graber

Jordan Lee Boyd-Graber publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and General Social Sciences, on topics such as Topic Modeling, Natural Language Processing Techniques and Multimodal Machine Learning Applications.

ORCID 0000-0002-7770-4431 OpenAlex
World rank
#130,976
of 7,710,434 ranked researchers
Rank in United States
#49,741
of 1,627,098
Works
217
Citations
8.7k
Citations per work
40.3
h-index
41
i10-index 94Top 2% in Artificial Intelligence
Jordan Lee Boyd-Graber

What does Jordan Lee Boyd-Graber research?

Jordan Lee Boyd-Graber's work is mostly in Computer Science (76%), Social Sciences (6%), Physics and Astronomy (1%).

Physical Sciences: 168 works · 77.3% of their workComputer Science: 165 works · 76.0% of their workArtificial Intelligence: 143 works · 65.7% of their workComputer Vision and Pattern Recognition: 16 works · 7.5% of their workInformation Systems: 6 works · 2.8% of their workPhysics and Astronomy: 3 works · 1.3% of their workStatistical and Nonlinear Physics: 3 works · 1.3% of their workEverything else: 36 works · 16.7% of their workSocial Sciences: 13 works · 6.0% of their workSocial Sciences: 13 works · 6.0% of their workGeneral Social Sciences: 10 works · 4.4% of their workSociology and Political Science: 3 works · 1.6% of their workTHEIR WORKJordan LeeBoyd-Graber217works
Physical Sciences77%Social Sciences6%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science16576.0%
    2. Physics and Astronomy31.3%
  2. Everything else3616.7%
    1. Social Sciences136.0%
  1. 1Artificial Intelligence66% of their works
  2. 2Computer Vision and Pattern Recognition8% of their works
  3. 3General Social Sciences4% of their works
  4. 4Information Systems3% of their works
  5. 5Sociology and Political Science2% of their works
  6. 6Statistical and Nonlinear Physics1% of their works

Research topics

Which keywords describe Jordan Lee Boyd-Graber's research?

The keywords of their largest research topics: Neural Networks, Topic Modeling, Information Retrieval, Text Classification, Machine Translation, Word Representation, Neural Machine Translation and Statistical Machine Translation.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 20 words, with their numbers
  1. Neural Networks1963 topics
  2. Topic Modeling1662 topics
  3. Information Retrieval1552 topics
  4. Text Classification1552 topics
  5. Machine Translation1391 topic
  6. Word Representation1391 topic
  7. Neural Machine Translation911 topic
  8. Statistical Machine Translation911 topic
  9. Image Captioning291 topic
  10. Visual Question Answering291 topic
  11. Automatic281 topic
  12. Extraction281 topic
  13. Computational Text Analysis271 topic
  14. Machine Learning271 topic
  15. Interpretable Models181 topic
  16. Machine Learning Interpretability181 topic
  17. Feature Selection161 topic
  18. Multi-label Learning161 topic
  19. Markov Decision Processes131 topic
  20. Spoken Dialogue Systems131 topic

Where does Jordan Lee Boyd-Graber work?

Jordan Lee Boyd-Graber's main affiliation in the publication record is University of Maryland, College Park, United States.

How many publications and citations does Jordan Lee Boyd-Graber have?

OpenAlex counts 217 works and 8,738 citations for Jordan Lee Boyd-Graber, an h-index of 41, ranking #130,976 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Other researchers named Jordan Lee Boyd-Graber

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

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