Noé Casas

28 total papers · 476 total citations
16 papers, 66 citations indexed

About

Noé Casas is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Ophthalmology. According to data from OpenAlex, Noé Casas has authored 16 papers receiving a total of 66 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Ophthalmology. Recurrent topics in Noé Casas's work include Natural Language Processing Techniques (10 papers), Topic Modeling (9 papers) and Text Readability and Simplification (3 papers). Noé Casas is often cited by papers focused on Natural Language Processing Techniques (10 papers), Topic Modeling (9 papers) and Text Readability and Simplification (3 papers). Noé Casas collaborates with scholars based in Spain and United States. Noé Casas's co-authors include Marta R. Costa‐jussà, José A. R. Fonollosa, Carlos Escolano, J. A. Pajares, Mihael Arčan, Maraim Masoud, Gorka Labaka, Eneko Agirre, Bharathi Raja Chakravarthi and Mikel Artetxe and has published in prestigious journals such as Neural Computing and Applications, Natural Language Engineering and ACM Transactions on Asian and Low-Resource Language Information Processing.

In The Last Decade

Noé Casas

16 papers receiving 63 citations

Author Peers

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

Author Last Decade Papers Cites
Noé Casas 46 19 5 5 4 16 66
Quang Pham 51 1.1× 27 1.4× 6 1.2× 2 0.5× 10 67
Shweta Jain 15 0.3× 19 1.0× 5 1.0× 2 0.5× 14 58
Kien Do 51 1.1× 22 1.2× 2 0.4× 5 1.3× 14 64
Tom Rainforth 31 0.7× 12 0.6× 3 0.6× 5 1.3× 14 73
Badri N. Patro 49 1.1× 47 2.5× 5 1.0× 2 0.5× 17 90
Tri Dao 37 0.8× 20 1.1× 9 1.8× 2 0.5× 12 74
Monalisa Nayak 25 0.5× 10 0.5× 1 0.2× 7 1.4× 4 1.0× 14 56
Emilie Morvant 54 1.2× 18 0.9× 1 0.2× 2 0.4× 4 1.0× 10 68
Hasan Abed Al Kader Hammoud 42 0.9× 28 1.5× 2 0.4× 3 0.8× 11 81
Chicheng Zhang 65 1.4× 10 0.5× 8 1.6× 2 0.5× 15 79

Countries citing papers authored by Noé Casas

Since Specialization
Citations

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

Fields of papers citing papers by Noé Casas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Noé Casas

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

All Works

Loading papers...

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026