Tristan Naumann

96 total papers · 8.2k total citations
34 papers, 2.9k citations indexed

About

Tristan Naumann is a scholar working on Artificial Intelligence, Molecular Biology and Health Informatics. According to data from OpenAlex, Tristan Naumann has authored 34 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 11 papers in Molecular Biology and 7 papers in Health Informatics. Recurrent topics in Tristan Naumann's work include Machine Learning in Healthcare (16 papers), Topic Modeling (13 papers) and Biomedical Text Mining and Ontologies (10 papers). Tristan Naumann is often cited by papers focused on Machine Learning in Healthcare (16 papers), Topic Modeling (13 papers) and Biomedical Text Mining and Ontologies (10 papers). Tristan Naumann collaborates with scholars based in United States, United Kingdom and Canada. Tristan Naumann's co-authors include Hoifung Poon, Naoto Usuyama, 裕二 池谷, Jianfeng Gao, Matthew B. A. McDermott, Robert Tinn, Hao Cheng, Michael Lucas, Xiaodong Liu and John R. Murphy and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nature Methods and Science Translational Medicine.

In The Last Decade

Tristan Naumann

34 papers receiving 2.8k citations

Hit Papers

Domain-Specific Language ... 2019 2026 2021 2023 2021 2019 250 500 750

Author Peers

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

Author Last Decade Papers Cites
Tristan Naumann 2.1k 907 424 370 293 34 2.9k
Jianying Hu 1.0k 0.5× 584 0.6× 330 0.8× 491 1.3× 233 0.8× 77 2.6k
Sijia Liu 1.7k 0.8× 900 1.0× 211 0.5× 346 0.9× 253 0.9× 157 3.2k
Thomas A. Lasko 857 0.4× 448 0.5× 228 0.5× 347 0.9× 273 0.9× 56 2.4k
Volodymyr Kuleshov 1.0k 0.5× 442 0.5× 642 1.5× 295 0.8× 733 2.5× 29 2.8k
Kirk Roberts 1.9k 0.9× 1.0k 1.1× 175 0.4× 245 0.7× 277 0.9× 134 2.9k
Donghyeon Kim 2.9k 1.4× 1.7k 1.9× 272 0.6× 159 0.4× 231 0.8× 24 4.0k
Sungdong Kim 2.7k 1.3× 1.5k 1.6× 266 0.6× 149 0.4× 225 0.8× 41 3.9k
Edward Choi 1.8k 0.8× 326 0.4× 240 0.6× 711 1.9× 270 0.9× 48 2.5k
Jinhyuk Lee 3.1k 1.5× 2.0k 2.2× 272 0.6× 154 0.4× 239 0.8× 43 4.5k
Cao Xiao 1.9k 0.9× 1.3k 1.4× 167 0.4× 630 1.7× 233 0.8× 124 4.1k

Countries citing papers authored by Tristan Naumann

Since Specialization
Citations

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

Fields of papers citing papers by Tristan Naumann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tristan Naumann

This figure shows the co-authorship network connecting the top 25 collaborators of Tristan Naumann. A scholar is included among the top collaborators of Tristan Naumann 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 Tristan Naumann. Tristan Naumann 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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