Mengnan Du

5.2k total citations · 3 hit papers
62 papers, 2.5k citations indexed

About

Mengnan Du is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Safety Research. According to data from OpenAlex, Mengnan Du has authored 62 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 20 papers in Computer Vision and Pattern Recognition and 8 papers in Safety Research. Recurrent topics in Mengnan Du's work include Topic Modeling (16 papers), Explainable Artificial Intelligence (XAI) (15 papers) and Adversarial Robustness in Machine Learning (13 papers). Mengnan Du is often cited by papers focused on Topic Modeling (16 papers), Explainable Artificial Intelligence (XAI) (15 papers) and Adversarial Robustness in Machine Learning (13 papers). Mengnan Du collaborates with scholars based in United States, China and United Kingdom. Mengnan Du's co-authors include Xia Hu, Ninghao Liu, Fan Yang, Zijian Zhang, Haofan Wang, Zifan Wang, Piotr Mardziel, Sirui Ding, Na Zou and Yuening Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Communications of the ACM.

In The Last Decade

Mengnan Du

53 papers receiving 2.5k citations

Hit Papers

Techniques for interpretable machine learning 2019 2026 2021 2023 2019 2020 2024 250 500 750
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.

  1. Techniques for interpretable machine learning
    2019 773 citations Mengnan Du, Ninghao Liu et al. profile →
  2. Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
    2020 759 citations Haofan Wang, Zifan Wang et al. profile →
  3. Explainability for Large Language Models: A Survey
    2024 232 citations Haiyan Zhao, Hanjie Chen et al. ACM Transactions on Intelligent Systems and Technology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mengnan Du United States 16 1.5k 607 188 162 150 62 2.5k
Sebastian Lapuschkin Germany 16 1.6k 1.1× 610 1.0× 231 1.2× 152 0.9× 210 1.4× 34 2.8k
Jaesik Choi South Korea 18 996 0.7× 380 0.6× 109 0.6× 157 1.0× 178 1.2× 87 2.1k
Christin Seifert Germany 19 807 0.5× 393 0.6× 135 0.7× 105 0.6× 153 1.0× 100 1.5k
Ninghao Liu United States 19 1.4k 0.9× 253 0.4× 72 0.4× 147 0.9× 145 1.0× 71 2.2k
Karl Kumbier United States 6 1.9k 1.2× 249 0.4× 172 0.9× 91 0.6× 152 1.0× 13 3.1k
Jennifer Wortman Vaughan United States 23 2.4k 1.6× 920 1.5× 210 1.1× 98 0.6× 190 1.3× 68 3.9k
José M. Alonso Spain 28 1.6k 1.1× 199 0.3× 131 0.7× 178 1.1× 284 1.9× 117 3.1k
Simone Scardapane Italy 25 1.6k 1.0× 558 0.9× 109 0.6× 252 1.6× 96 0.6× 96 2.7k
Sebastian Bach South Korea 2 1.6k 1.1× 468 0.8× 218 1.2× 111 0.7× 169 1.1× 2 2.5k

Countries citing papers authored by Mengnan Du

Since Specialization
Citations

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

Fields of papers citing papers by Mengnan Du

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mengnan Du

This figure shows the co-authorship network connecting the top 25 collaborators of Mengnan Du. A scholar is included among the top collaborators of Mengnan Du 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 Mengnan Du. Mengnan Du 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
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Du, Mengnan, et al.. (2024). Mitigating social biases of pre-trained language models via contrastive self-debiasing with double data augmentation. Artificial Intelligence. 332. 104143–104143. 3 indexed citations
4.
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Jin, Mingyu, Haiyan Zhao, Wenyue Hua, et al.. (2024). The Impact of Reasoning Step Length on Large Language Models. 1830–1842. 7 indexed citations
6.
Feng, Qizhang, Mengnan Du, Na Zou, & Xia Hu. (2024). Fair Machine Learning in Healthcare: A Survey. IEEE Transactions on Artificial Intelligence. 6(3). 493–507. 2 indexed citations
7.
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.
8.
Xiong, Haoyi, 将尚 渡辺, Yuchen Li, et al.. (2024). When Search Engine Services Meet Large Language Models: Visions and Challenges. IEEE Transactions on Services Computing. 17(6). 4558–4577. 19 indexed citations
9.
Zou, Na, Mengnan Du, Weifu Chen, et al.. (2024). Unifying Fourteen Post-Hoc Attribution Methods With Taylor Interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(7). 4625–4640. 9 indexed citations
10.
Zhao, Haiyan, Hanjie Chen, Fan Yang, et al.. (2024). Explainability for Large Language Models: A Survey. ACM Transactions on Intelligent Systems and Technology. 15(2). 1–38. 232 indexed citations breakdown →
11.
Tang, Ruixiang, et al.. (2023). Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks. 4315–4319. 4 indexed citations
12.
Li, Tianlin, Zhiming Li, Anran Li, et al.. (2023). Fairness via Group Contribution Matching. 436–445. 7 indexed citations
13.
Yuan, Hao, Fan Yang, Mengnan Du, Shuiwang Ji, & Xia Hu. (2021). Towards structured NLP interpretation via graph explainers. SHILAP Revista de lepidopterología. 2(4). 2 indexed citations
14.
Zhang, Zhiying, Mengnan Du, Dayong Gu, et al.. (2021). 1378P A single-center, prospective, open-label, single-arm trial of sintilimab with paclitaxel and carboplatin as a neoadjuvant therapy for esophageal squamous carcinoma. Annals of Oncology. 32. S1042–S1043. 6 indexed citations
15.
Wang, Haofan, Zifan Wang, Mengnan Du, et al.. (2020). Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks. 111–119. 759 indexed citations breakdown →
16.
Du, Mengnan, Fan Yang, Na Zou, & Xia Hu. (2020). Fairness in Deep Learning: A Computational Perspective. IEEE Intelligent Systems. 36(4). 25–34. 123 indexed citations
17.
Liu, Ninghao, Mengnan Du, & Xia Hu. (2020). Adversarial Machine Learning: An Interpretation Perspective. arXiv (Cornell University). 6 indexed citations
18.
Liu, Ninghao, et al.. (2020). A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter. BMC Medical Informatics and Decision Making. 20(S4). 254–254. 11 indexed citations
19.
Du, Mengnan, et al.. (2019). Towards Generalizable Forgery Detection with Locality-aware AutoEncoder.. arXiv (Cornell University). 21 indexed citations
20.
Wang, Haofan, Mengnan Du, Fan Yang, & Zijian Zhang. (2019). Score-CAM: Improved Visual Explanations Via Score-Weighted Class Activation Mapping.. arXiv (Cornell University). 19 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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