Ann Yuan

1.5k total citations · 1 hit paper
10 papers, 444 citations indexed

About

Ann Yuan is a scholar working on Artificial Intelligence, Information Systems and Communication. According to data from OpenAlex, Ann Yuan has authored 10 papers receiving a total of 444 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Information Systems and 1 paper in Communication. Recurrent topics in Ann Yuan's work include Topic Modeling (6 papers), Natural Language Processing Techniques (3 papers) and Software Engineering Research (2 papers). Ann Yuan is often cited by papers focused on Topic Modeling (6 papers), Natural Language Processing Techniques (3 papers) and Software Engineering Research (2 papers). Ann Yuan collaborates with scholars based in United States, United Kingdom and Taiwan. Ann Yuan's co-authors include Emily Reif, Andy Coenen, Daphne Ippolito, Fernanda Viégas, Martin Wattenberg, Adam Pearce, Been Kim, Kuo‐Liong Chien, Min‐Shan Tsai and Wen‐Jone Chen and has published in prestigious journals such as Communications of the ACM, Intensive Care Medicine and arXiv (Cornell University).

In The Last Decade

Ann Yuan

9 papers receiving 426 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.

Wordcraft: Story Writing With Large Language Models

2022 174 citations Ann Yuan, Andy Coenen et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ann Yuan United States	6	233	96	45	41	36	10	444
Kosuke Nakajima Japan	10	134 0.6×	26 0.3×	54 1.2×	19 0.5×	41 1.1×	36	554
Sergio Guillén Spain	11	51 0.2×	21 0.2×	77 1.7×	34 0.8×	67 1.9×	43	366
Daniel Miller United States	9	157 0.7×	9 0.1×	31 0.7×	49 1.2×	36 1.0×	27	380
Daniele Di Mitri Germany	12	273 1.2×	18 0.2×	33 0.7×	102 2.5×	12 0.3×	29	660
Mahmoud Elbattah France	13	133 0.6×	25 0.3×	39 0.9×	12 0.3×	8 0.2×	32	483
Adrienne Kline Canada	8	100 0.4×	6 0.1×	16 0.4×	13 0.3×	45 1.3×	20	415
Harald Burgsteiner Austria	9	125 0.5×	9 0.1×	45 1.0×	25 0.6×	24 0.7×	17	346
Leandro Miranda Brazil	8	42 0.2×	61 0.6×	101 2.2×	7 0.2×	44 1.2×	18	263
Randall A. Brown United States	8	109 0.5×	4 0.0×	90 2.0×	24 0.6×	52 1.4×	12	304

Countries citing papers authored by Ann Yuan

Since Specialization

Citations

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

Fields of papers citing papers by Ann Yuan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ann Yuan

This figure shows the co-authorship network connecting the top 25 collaborators of Ann Yuan. A scholar is included among the top collaborators of Ann Yuan 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 Ann Yuan. Ann Yuan 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:

10 of 10 papers shown

1.

Petridis, Savvas, et al.. (2024). ConstitutionalExperts: Training a Mixture of Principle-based Prompts. 574–582. 2 indexed citations

2.

Goodman, Steven M., Erin Buehler, Patrick Clary, et al.. (2024). LaMPost: AI Writing Assistance for Adults with Dyslexia Using Large Language Models. Communications of the ACM. 67(9). 80–89.

3.

Mozes, Maximilian, Jessica D. Hoffmann, Katrin Tomanek, et al.. (2023). Towards Agile Text Classifiers for Everyone. 400–414. 2 indexed citations

4.

Ippolito, Daphne, Liam Dugan, Emily Reif, et al.. (2022). The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank. 2421–2432. 2 indexed citations

5.

Saveski, Martin, Nabeel Gillani, Ann Yuan, Prashanth Vijayaraghavan, & Deb Roy. (2022). Perspective-Taking to Reduce Affective Polarization on Social Media. Proceedings of the International AAAI Conference on Web and Social Media. 16. 885–895. 12 indexed citations

6.

Yuan, Ann, Andy Coenen, Emily Reif, & Daphne Ippolito. (2022). Wordcraft: Story Writing With Large Language Models. 841–852. 174 indexed citations breakdown →

7.

Tenney, Ian, James Wexler, Jasmijn Bastings, et al.. (2020). The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. 107–118. 73 indexed citations

8.

Smilkov, Daniel, Nikhil Thorat, Ann Yuan, et al.. (2019). TensorFlow.js: Machine Learning for the Web and Beyond. arXiv (Cornell University). 1. 309–321. 5 indexed citations

9.

Reif, Emily, Ann Yuan, Martin Wattenberg, et al.. (2019). Visualizing and Measuring the Geometry of BERT. Neural Information Processing Systems. 32. 8592–8600. 70 indexed citations

10.

Chang, Wei‐Tien, Matthew Huei‐Ming, Kuo‐Liong Chien, et al.. (2006). Postresuscitation myocardial dysfunction: correlated factors and prognostic implications. Intensive Care Medicine. 33(1). 88–95. 104 indexed citations

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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