Bailey Kuehl

1.2k total citations · 1 hit paper
18 papers, 799 citations indexed

About

Bailey Kuehl is a scholar working on Artificial Intelligence, Molecular Biology and Mechanical Engineering. According to data from OpenAlex, Bailey Kuehl has authored 18 papers receiving a total of 799 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 6 papers in Molecular Biology and 2 papers in Mechanical Engineering. Recurrent topics in Bailey Kuehl's work include Topic Modeling (13 papers), Advanced Text Analysis Techniques (7 papers) and Natural Language Processing Techniques (6 papers). Bailey Kuehl is often cited by papers focused on Topic Modeling (13 papers), Advanced Text Analysis Techniques (7 papers) and Natural Language Processing Techniques (6 papers). Bailey Kuehl collaborates with scholars based in United States, Mexico and Australia. Bailey Kuehl's co-authors include K.M. Bertsch, Dan J. Thoma, G. Meric de Bellefon, Lucy Lu Wang, Kyle Lo, Arman Cohan, Akihide Nagao, Iz Beltagy, Behzad Rankouhi and Jay DeYoung and has published in prestigious journals such as Acta Materialia, Corrosion Science and Transactions of the Association for Computational Linguistics.

In The Last Decade

Bailey Kuehl

17 papers receiving 783 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Origin of dislocation structures in an additively manufactured austenitic stainless steel 316L

2020 569 citations K.M. Bertsch, G. Meric de Bellefon et al. Acta Materialia profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Bailey Kuehl United States	9	595	173	150	138	95	18	799
Martin Riedler Austria	12	166 0.3×	33 0.2×	11 0.1×	113 0.8×	55 0.6×	29	424
Yuhua Sun China	14	46 0.1×	52 0.3×	88 0.6×	89 0.6×	14 0.1×	27	481
Yonggang Deng China	17	338 0.6×	4 0.0×	343 2.3×	271 2.0×	13 0.1×	63	836
Masayuki Okamoto Japan	13	79 0.1×	154 0.9×	28 0.2×	22 0.2×	5 0.1×	110	564
Hongwei Cui China	10	123 0.2×	52 0.3×	8 0.1×	21 0.2×	24 0.3×	39	273
Roya Darabi Iran	11	263 0.4×	70 0.4×	9 0.1×	113 0.8×	41 0.4×	62	460

Countries citing papers authored by Bailey Kuehl

Since Specialization

Citations

This map shows the geographic impact of Bailey Kuehl'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 Bailey Kuehl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bailey Kuehl more than expected).

Fields of papers citing papers by Bailey Kuehl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bailey Kuehl. 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 Bailey Kuehl. The network helps show where Bailey Kuehl may publish in the future.

Co-authorship network of co-authors of Bailey Kuehl

This figure shows the co-authorship network connecting the top 25 collaborators of Bailey Kuehl. A scholar is included among the top collaborators of Bailey Kuehl 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 Bailey Kuehl. Bailey Kuehl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

18 of 18 papers shown

Gu, Yuling, et al.. (2025). OLMES: A Standard for Language Model Evaluations. 5005–5033. 1 indexed citations

D’Arcy, Mike, et al.. (2024). ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews. 6985–7001. 1 indexed citations

Hsu, Chao-Chun, Bailey Kuehl, Chenhao Tan, et al.. (2024). CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support. 118–132. 3 indexed citations

Lo, Kyle, et al.. (2024). KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions. 12969–12990. 1 indexed citations

Lo, Kyle, Zejiang Shen, Benjamin J. Newman, et al.. (2023). PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents. 495–507. 2 indexed citations

Krishna, Kalpesh, Bailey Kuehl, Mohit Iyyer, et al.. (2023). LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization. 1650–1669. 16 indexed citations

Wang, Lucy Lu, et al.. (2023). Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations. PubMed. 2023. 9871–9889. 7 indexed citations

Kuehl, Bailey, et al.. (2023). S2abEL: A Dataset for Entity Linking from Scientific Tables. 3089–3101.

Lo, Kyle, Chandra Bhagavatula, Bailey Kuehl, et al.. (2022). ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts. 200–213. 3 indexed citations

10.

Wadden, David, Kyle Lo, Bailey Kuehl, et al.. (2022). Generating Scientific Claims for Zero-Shot Scientific Fact Checking. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2448–2460. 22 indexed citations

11.

Wadden, David, Kyle Lo, Bailey Kuehl, et al.. (2022). SciFact-Open: Towards open-domain scientific claim verification. 4719–4734. 14 indexed citations

12.

Lauscher, Anne, et al.. (2022). MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1875–1889. 22 indexed citations

13.

Lahav, Dan, Bailey Kuehl, Sravanthi Parasa, et al.. (2022). A Search Engine for Discovery of Scientific Challenges and Directions. Proceedings of the AAAI Conference on Artificial Intelligence. 36(11). 11982–11990. 15 indexed citations

14.

Shen, Zejiang, Kyle Lo, Lucy Lu Wang, et al.. (2022). VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups. Transactions of the Association for Computational Linguistics. 10. 376–392. 14 indexed citations

15.

DeYoung, Jay, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, & Lucy Lu Wang. (2021). MSˆ2: Multi-Document Summarization of Medical Studies. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 7494–7513. 47 indexed citations

16.

Wang, Lucy Lu, et al.. (2021). SciA11y: Converting Scientific Papers to Accessible HTML. 1–4. 8 indexed citations

17.

Bertsch, K.M., Akihide Nagao, Behzad Rankouhi, Bailey Kuehl, & Dan J. Thoma. (2021). Hydrogen embrittlement of additively manufactured austenitic stainless steel 316 L. Corrosion Science. 192. 109790–109790. 54 indexed citations

18.

Bertsch, K.M., G. Meric de Bellefon, Bailey Kuehl, & Dan J. Thoma. (2020). Origin of dislocation structures in an additively manufactured austenitic stainless steel 316L. Acta Materialia. 199. 19–33. 569 indexed citations breakdown →

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.

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