Tu Vu

50 total papers · 748 total citations
15 papers, 321 citations indexed

About

Tu Vu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Tu Vu has authored 15 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Information Systems. Recurrent topics in Tu Vu's work include Topic Modeling (12 papers), Natural Language Processing Techniques (11 papers) and Multimodal Machine Learning Applications (4 papers). Tu Vu is often cited by papers focused on Topic Modeling (12 papers), Natural Language Processing Techniques (11 papers) and Multimodal Machine Learning Applications (4 papers). Tu Vu collaborates with scholars based in United States, Vietnam and United Kingdom. Tu Vu's co-authors include Noah Constant, Brian Lester, Daniel Cer, Rami Al‐Rfou, Mohit Iyyer, Son Bao Pham, Le-Minh Nguyen, Vu Tran, Quan Hung Tran and Tsendsuren Munkhdalai and has published in prestigious journals such as International Review of Education, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

In The Last Decade

Tu Vu

15 papers receiving 311 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Tu Vu	273	74	56	12	8	15	321
Zai Huang	277 1.0×	52 0.7×	83 1.5×	7 0.6×	4 0.5×	7	358
Katherine Lee	260 1.0×	42 0.6×	54 1.0×	6 0.5×	5 0.6×	13	355
Rui Liu	193 0.7×	41 0.6×	44 0.8×	4 0.3×	4 0.5×	16	287
Sarawoot Kongyoung	172 0.6×	35 0.5×	46 0.8×	4 0.3×	6 0.8×	20	293
Niklas Muennighoff	290 1.1×	51 0.7×	36 0.6×	2 0.2×	11 1.4×	8	357
Mohammed Y. Shakor	148 0.5×	25 0.3×	57 1.0×	4 0.3×	4 0.5×	15	305
Nabil Alami	219 0.8×	18 0.2×	70 1.3×	7 0.6×	2 0.3×	22	271
Wenhao Zhang	152 0.6×	38 0.5×	52 0.9×	20 1.7×	1 0.1×	12	229
Giuseppe Castellucci	287 1.1×	63 0.9×	57 1.0×	5 0.4×	2 0.3×	22	341
Eric Malmi	225 0.8×	37 0.5×	40 0.7×	2 0.2×	4 0.5×	25	302

Countries citing papers authored by Tu Vu

Since Specialization

Citations

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

Fields of papers citing papers by Tu Vu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tu Vu

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

All Works

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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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