Ruixiang Tang

1.4k citations
28 papers · 535 indexed · 2 hit papers · h-index 10
Topics
Topic Modeling (6 papers)Adversarial Robustness in Machine Learning (4 papers)Natural Language Processing Techniques (3 papers)
Partner nations
United StatesChinaSpain

In The Last Decade

Ruixiang Tang

25 papers receiving 515 citations

Hit Papers

Harnessing the Power of LLMs in Practice: A Survey on Cha...2024202620252024202450100150200

Peers

Ruixiang Tang
Comparison fields: 5 of 100
  • Artificial Intelligence 318
  • Computer Vision and Pattern Recognition 81
  • Information Systems 69
  • Health Informatics 56
  • Signal Processing 43
Replace Daniel Smilkov with:
Daniel Smilkov North Macedonia
James Wexler United States
Eric Wallace United States
Bing Yin United States
Haoming Jiang United States
Yiqi Wang China
Jiakai Tang China
Damai Dai China
Peng Qi China
Ruixiang Tang relative to Daniel Smilkov North Macedonia Daniel Smilkov's profile →
Citations per field
00.5×9.5×
Daniel Smilkov · 1×
Citations per year

Countries citing papers authored by Ruixiang Tang

Since Specialization
Citations

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

Fields of papers citing papers by Ruixiang Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruixiang Tang

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 0
4 6
5
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondbreakdown →
237
6 8
7 20
8 2
9
The Science of Detecting LLM-Generated Textbreakdown →
58
10 4
11 2
12 2
13 12
14 2
15 2
16 2
17 9
18 7
19 80
20 2

About Ruixiang Tang

Ruixiang Tang is a scholar working on Health Informatics, Microbiology and Artificial Intelligence, having authored 28 papers that have together received 535 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Adversarial Robustness in Machine Learning (4 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Health Informatics (56 citations), Artificial Intelligence (318 citations) and Signal Processing (43 citations). Ruixiang Tang has collaborated with scholars based in United States, China and Spain. Frequent co-authors include Xia Hu, Qizhang Feng, Hongye Jin, Jingfeng Yang, Haoming Jiang, Shaochen Zhong, Xiaotian Han, Bing Yin, Mengnan Du and Ninghao Liu. Their work appears in journals such as Communications of the ACM, Sensors and Chemical Engineering Science.

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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