Yada Pruksachatkun

2.0k total citations
8 papers, 372 citations indexed

About

Yada Pruksachatkun is a scholar working on Artificial Intelligence, General Health Professions and Safety Research. According to data from OpenAlex, Yada Pruksachatkun has authored 8 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in General Health Professions and 2 papers in Safety Research. Recurrent topics in Yada Pruksachatkun's work include Natural Language Processing Techniques (3 papers), Topic Modeling (3 papers) and Interpreting and Communication in Healthcare (2 papers). Yada Pruksachatkun is often cited by papers focused on Natural Language Processing Techniques (3 papers), Topic Modeling (3 papers) and Interpreting and Communication in Healthcare (2 papers). Yada Pruksachatkun collaborates with scholars based in United States, France and Switzerland. Yada Pruksachatkun's co-authors include Samuel R. Bowman, Felix Hill, Julian Michael, Alex Wang, Omer Levy, Nikita Nangia, Amanpreet Singh, Sachin R. Pendse, Amit Sharma and Jason Phang and has published in prestigious journals such as Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Neural Information Processing Systems and Findings of the Association for Computational Linguistics: ACL 2022.

In The Last Decade

Yada Pruksachatkun

7 papers receiving 342 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yada Pruksachatkun United States	7	301	80	35	33	31	8	372
Abhilasha Ravichander United States	8	281 0.9×	52 0.7×	128 3.7×	21 0.6×	81 2.6×	14	395
Stefan Schweter	4	312 1.0×	28 0.3×	36 1.0×	16 0.5×	44 1.4×	6	359
Mihael Arčan Ireland	12	415 1.4×	50 0.6×	25 0.7×	28 0.8×	52 1.7×	52	434
Radhika Mamidi India	12	501 1.7×	39 0.5×	83 2.4×	56 1.7×	62 2.0×	87	581
Adrián Pastor López-Monroy Mexico	11	261 0.9×	43 0.5×	48 1.4×	115 3.5×	83 2.7×	51	368
Dimitra Gkatzia United Kingdom	9	202 0.7×	48 0.6×	12 0.3×	9 0.3×	28 0.9×	33	248
Niyati Chhaya United States	8	159 0.5×	26 0.3×	31 0.9×	41 1.2×	34 1.1×	40	217
Lucie Flek Germany	12	251 0.8×	14 0.2×	61 1.7×	84 2.5×	67 2.2×	41	358
Maheen Farooqi Canada	3	211 0.7×	24 0.3×	39 1.1×	13 0.4×	18 0.6×	5	323
Kellie Webster United States	7	255 0.8×	31 0.4×	22 0.6×	7 0.2×	22 0.7×	11	328

Countries citing papers authored by Yada Pruksachatkun

Since Specialization

Citations

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

Fields of papers citing papers by Yada Pruksachatkun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yada Pruksachatkun

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

8 of 8 papers shown

1.

Gupta, Umang, Varun Kumar, Yada Pruksachatkun, et al.. (2022). Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal. Findings of the Association for Computational Linguistics: ACL 2022. 658–678. 16 indexed citations

2.

Verma, Apurv, et al.. (2022). Measuring Fairness of Text Classifiers via Prediction Sensitivity. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 5830–5842.

3.

Cao, Yang, Yada Pruksachatkun, Kai-Wei Chang, et al.. (2022). On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations. 561–570. 30 indexed citations

4.

Pruksachatkun, Yada, et al.. (2021). Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification. 9 indexed citations

5.

Pruksachatkun, Yada, Jason Phang, Haokun Liu, et al.. (2020). Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?. 5231–5247. 88 indexed citations

6.

Wang, Alex, Yada Pruksachatkun, Nikita Nangia, et al.. (2019). SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems. Neural Information Processing Systems. 32. 3261–3275. 166 indexed citations

7.

Pruksachatkun, Yada, Sachin R. Pendse, & Amit Sharma. (2019). Moments of Change. 1–13. 44 indexed citations

8.

Metaxas, Panagiotis & Yada Pruksachatkun. (2017). Manipulation of Search Engine Results during the 2016 US Congressional Elections. 19 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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