Dekai Wu

76 papers and 1.2k indexed citations i.

About

Dekai Wu is a scholar working on Artificial Intelligence, Signal Processing and Computational Theory and Mathematics. According to data from OpenAlex, Dekai Wu has authored 76 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Artificial Intelligence, 4 papers in Signal Processing and 3 papers in Computational Theory and Mathematics. Recurrent topics in Dekai Wu’s work include Natural Language Processing Techniques (66 papers), Topic Modeling (60 papers) and Speech and dialogue systems (17 papers). Dekai Wu is often cited by papers focused on Natural Language Processing Techniques (66 papers), Topic Modeling (60 papers) and Speech and dialogue systems (17 papers). Dekai Wu collaborates with scholars based in Hong Kong, United States and Sweden. Dekai Wu's co-authors include Marine Carpuat, Chi-kiu Lo, Pascale Fung, Grace Ngai, Weifeng Su, Andreas Stolcke, Yongsheng Yang, Joakim Nivre, Ondřej Bojar and David Chiang and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Behavioral and Brain Sciences and Artificial Intelligence Review.

In The Last Decade

Co-authorship network of co-authors of Dekai Wu i

Fields of papers citing papers by Dekai Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Dekai Wu

Since Specialization
Citations

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

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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2025