Ruixiang Tang

1.4k total citations · 2 hit papers
28 papers, 535 citations indexed

About

Ruixiang Tang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ruixiang Tang has authored 28 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ruixiang Tang's work include Topic Modeling (6 papers), Adversarial Robustness in Machine Learning (4 papers) and Natural Language Processing Techniques (3 papers). Ruixiang Tang is often cited by papers focused on Topic Modeling (6 papers), Adversarial Robustness in Machine Learning (4 papers) and Natural Language Processing Techniques (3 papers). Ruixiang Tang collaborates with scholars based in United States, China and Spain. Ruixiang Tang's co-authors include Xia Hu, Qizhang Feng, Hongye Jin, Jingfeng Yang, Haoming Jiang, Shaochen Zhong, Xiaotian Han, Bing Yin, Mengnan Du and Ninghao Liu and has published in prestigious journals such as Communications of the ACM, Sensors and Chemical Engineering Science.

In The Last Decade

Ruixiang Tang

25 papers receiving 515 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

2024 237 citations Jingfeng Yang, Hongye Jin et al. ACM Transactions on Knowledge Discovery from Data profile →
The Science of Detecting LLM-Generated Text

2024 58 citations Ruixiang Tang, Xia Hu et al. Communications of the ACM profile →

Peers

Ruixiang Tang

AI

CVPR

IS

HI

SP

Daniel Smilkov North Macedonia

Bing Yin United States

Peng Qi China

James Wexler United States

Haoming Jiang United States

Yiqi Wang China

Allam Mousa Palestinian Territory

Oğuzhan Topsakal United States

Aasim Zafar India

Jiakai Tang China

Daniel Smilkov North Macedonia

Ruixiang Tang

253 ×0.8

AI

145 ×1.8

CVPR

44 ×0.6

IS

69 ×1.2

HI

22 ×0.5

SP

Citations per year, relative to Ruixiang Tang Ruixiang Tang (= 1×) peers Daniel Smilkov

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Zhong, Shaochen, Xinghuai Sun, Min Tian, et al.. (2025). LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem. 23009–23047.

2.

Li, Yanshu, et al.. (2025). TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration. 736–763. 1 indexed citations

3.

Lai, Kwei-Herng, et al.. (2024). Fair-RGNN: Mitigating Relational Bias on Knowledge Graphs. ACM Transactions on Knowledge Discovery from Data. 19(2). 1–18.

4.

Diao, Huaian, et al.. (2024). Unique determination by a single far-field measurement for an inverse elastic problem. Inverse Problems and Imaging. 18(6). 1405–1430. 6 indexed citations

5.

Yang, Jingfeng, Hongye Jin, Ruixiang Tang, et al.. (2024). Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond. ACM Transactions on Knowledge Discovery from Data. 18(6). 1–32. 237 indexed citations breakdown →

6.

Hua, Wenyue, Mingyu Jin, Zelong Li, et al.. (2024). TrustAgent: Towards Safe and Trustworthy LLM-based Agents. 10000–10016. 8 indexed citations

7.

Tang, Ruixiang, et al.. (2024). SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization. Journal of Biomedical Informatics. 151. 104606–104606. 20 indexed citations

8.

Tang, Ruixiang, Zheng Zou, Ji Xu, et al.. (2024). Simulation of the L-valve in the circulating fluidized bed with a coarse-grained discrete particle method. Particuology. 90. 266–280. 2 indexed citations

9.

Tang, Ruixiang, et al.. (2024). The Science of Detecting LLM-Generated Text. Communications of the ACM. 67(4). 50–59. 58 indexed citations breakdown →

10.

Tang, Ruixiang, et al.. (2023). Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks. 4315–4319. 4 indexed citations

11.

Lai, Kwei-Herng, Daochen Zha, Ruixiang Tang, et al.. (2023). DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research. PubMed. 2023. 5021–5025. 2 indexed citations

12.

Gao, Zhan, Ling Cao, Meng Yang, et al.. (2023). Analysis of coexisting pathogens in nasopharyngeal swabs from COVID-19. Frontiers in Cellular and Infection Microbiology. 13. 1140548–1140548. 2 indexed citations

13.

Tang, Ruixiang, et al.. (2023). Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning. 4645–4657. 12 indexed citations

14.

Tang, Ruixiang, et al.. (2023). A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation. Sensors. 23(8). 3834–3834. 2 indexed citations

15.

Liu, Xiaojin, et al.. (2022). Two chronically misdiagnosed patients infected with Nocardia cyriacigeorgica accurately diagnosed by whole genome resequencing. Frontiers in Cellular and Infection Microbiology. 12. 1032669–1032669. 2 indexed citations

16.

Du, Mengnan, Ruixiang Tang, Weijie Fu, & Xia Hu. (2022). Towards Debiasing DNN Models from Spurious Feature Influence. Proceedings of the AAAI Conference on Artificial Intelligence. 36(9). 9521–9528. 2 indexed citations

17.

Tang, Ruixiang, et al.. (2022). Defense Against Explanation Manipulation. Frontiers in Big Data. 5. 704203–704203. 9 indexed citations

18.

Du, Mengnan, Chao Fan, Ruixiang Tang, et al.. (2021). Understanding Social Biases Behind Location Names in Contextual Word Embedding Models. IEEE Transactions on Computational Social Systems. 9(2). 458–468. 7 indexed citations

19.

Tang, Ruixiang, Mengnan Du, Ninghao Liu, Fan Yang, & Xia Hu. (2020). An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks. 218–228. 80 indexed citations

20.

Tang, Ruixiang, et al.. (2015). Saliency Detection Integrated with Depth Information. Advances in computer science research. 2 indexed citations

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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