Reid Pryzant
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Sociology and Political Science
- Management Science and Operations Research
- Co-authors
- Dan JurafskyDiyi YangDenny BritzQuoc V. LeSadao KurohashiChenguang ZhuMichael ZengDan Iter
- Topics
- Topic Modeling (9 papers)Natural Language Processing Techniques (8 papers)Multimodal Machine Learning Applications (3 papers)
- Journals
- Proceedings of the National Academy of SciencesTransactions of the Association for Computational LinguisticsProceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesGermanyJapan
In The Last Decade
Reid Pryzant
15 papers receiving 428 citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 324
- Computer Vision and Pattern Recognition 76
- Information Systems 53
- Sociology and Political Science 44
- Management Science and Operations Research 18
Countries citing papers authored by Reid Pryzant
This map shows the geographic impact of Reid Pryzant's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Reid Pryzant with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Reid Pryzant more than expected).
Fields of papers citing papers by Reid Pryzant
This network shows the impact of papers produced by Reid Pryzant. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Reid Pryzant. The network helps show where Reid Pryzant may publish in the future.
Co-authorship network of co-authors of Reid Pryzant
This figure shows the co-authorship network connecting the top 25 collaborators of Reid Pryzant. A scholar is included among the top collaborators of Reid Pryzant based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Reid Pryzant. Reid Pryzant is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 24 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 13 | |
| 5 | 3 | |
| 6 | 20 | |
| 7 | 4 | |
| 8 | 55 | |
| 9 | Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondbreakdown → | 120 |
| 10 | 69 | |
| 11 | 28 | |
| 12 | 11 | |
| 13 | Predicting Sales from the Language of Product Descriptions. | 19 |
| 14 | 55 | |
| 15 | 24 |
About Reid Pryzant
Reid Pryzant is a scholar working on General Social Sciences, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 15 papers that have together received 448 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (8 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Artificial Intelligence (324 citations), Health Informatics (11 citations) and General Social Sciences (16 citations). Reid Pryzant has collaborated with scholars based in United States, Germany and Japan. Frequent co-authors include Dan Jurafsky, Diyi Yang, Denny Britz, Quoc V. Le, Sadao Kurohashi, Chenguang Zhu, Michael Zeng, Dan Iter, Margaret E. Roberts and Justin Grimmer. Their work appears in journals such as Proceedings of the National Academy of Sciences, Transactions of the Association for Computational Linguistics and Proceedings of the AAAI Conference on Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.