Juan Zhai

2.2k total citations · 1 hit paper
56 papers, 1.2k citations indexed

About

Juan Zhai is a scholar working on Artificial Intelligence, Software and Signal Processing. According to data from OpenAlex, Juan Zhai has authored 56 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 18 papers in Software and 10 papers in Signal Processing. Recurrent topics in Juan Zhai's work include Software Testing and Debugging Techniques (16 papers), Advanced Malware Detection Techniques (10 papers) and Adversarial Robustness in Machine Learning (7 papers). Juan Zhai is often cited by papers focused on Software Testing and Debugging Techniques (16 papers), Advanced Malware Detection Techniques (10 papers) and Adversarial Robustness in Machine Learning (7 papers). Juan Zhai collaborates with scholars based in China, United States and Netherlands. Juan Zhai's co-authors include Shiqing Ma, Xiangyu Zhang, Yousra Aafer, Wen‐Chuan Lee, Yingqi Liu, Weihang Wang, Xiangyu Zhang, Zhenting Wang, Chao Shen and Tom O. Abney and has published in prestigious journals such as The Science of The Total Environment, ACM Computing Surveys and Biology of Reproduction.

In The Last Decade

Juan Zhai

47 papers receiving 1.2k citations

Hit Papers

Trojaning Attack on Neural Networks 2018 2026 2020 2023 2018 200 400 600
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. Trojaning Attack on Neural Networks
    2018 611 citations Shiqing Ma, Yousra Aafer et al. View profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juan Zhai China 16 757 297 225 198 176 56 1.2k
S. Karthikeyan United States 13 428 0.6× 713 2.4× 223 1.0× 562 2.8× 243 1.4× 38 1.3k
Olivier Pereira Belgium 17 606 0.8× 54 0.2× 190 0.8× 439 2.2× 192 1.1× 59 1.2k
Xinyu Wang China 18 369 0.5× 48 0.2× 317 1.4× 279 1.4× 160 0.9× 67 943
Na Meng United States 16 240 0.3× 517 1.7× 893 4.0× 438 2.2× 13 0.1× 87 1.2k
C. D. Jaidhar India 16 220 0.3× 216 0.7× 295 1.3× 370 1.9× 81 0.5× 52 808
Zihao Ye China 12 375 0.5× 22 0.1× 105 0.5× 105 0.5× 223 1.3× 38 740

Countries citing papers authored by Juan Zhai

Since Specialization
Citations

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

Fields of papers citing papers by Juan Zhai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juan Zhai

This figure shows the co-authorship network connecting the top 25 collaborators of Juan Zhai. A scholar is included among the top collaborators of Juan Zhai 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 Juan Zhai. Juan Zhai 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
1.
Zhai, Juan, Hengyang Mao, Bin He, et al.. (2025). A review of recent development in the enhancement mechanism of catalytic membranes for wastewater treatment. 4(1). 79–98. 2 indexed citations
2.
Zhang, Pingping, Hengyang Mao, Shouyong Zhou, et al.. (2025). Robust Fe3O4@attapulgite-intercalated carboxylated graphene oxide composite membrane for efficient and stable oil-in-water emulsion separation. Colloids and Surfaces A Physicochemical and Engineering Aspects. 715. 136605–136605.
3.
Zhai, Juan, Zihan Li, Hengyang Mao, et al.. (2024). Multifunctional membranes with super-wetting interface and in-situ Fenton-like sites for efficient oil-in-water emulsion separation. Separation and Purification Technology. 354. 129505–129505. 9 indexed citations
4.
Zhai, Juan, et al.. (2024). Merlin: Multi-tier Optimization of eBPF Code for Performance and Compactness. 639–653. 7 indexed citations
5.
Nan, Yuhong, et al.. (2024). Understanding Privacy Risks of Intelligent Connected Vehicles Through Their Companion Mobile Apps. IEEE Internet of Things Journal. 11(20). 33683–33695.
6.
Shen, Chao, et al.. (2024). An Automated Monitoring and Repairing System for DNN Training. IEEE Transactions on Dependable and Secure Computing. 22(2). 1655–1673.
7.
Zhang, Quanjun, et al.. (2024). Machine Translation Testing via Syntactic Tree Pruning. ACM Transactions on Software Engineering and Methodology. 33(5). 1–39. 1 indexed citations
8.
Fang, Chunrong, Weisong Sun, Daoyuan Wu, et al.. (2024). Machine Learning for Actionable Warning Identification: A Comprehensive Survey. ACM Computing Surveys. 57(2). 1–35. 2 indexed citations
9.
10.
Wang, Shen, et al.. (2023). Lycopene Improves Bone Quality in SAMP6 Mice by Inhibiting Oxidative Stress, Cellular Senescence, and the SASP. Molecular Nutrition & Food Research. 67(24). e2300330–e2300330. 12 indexed citations
11.
Wang, Zhenting, Juan Zhai, & Shiqing Ma. (2022). BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15054–15063. 45 indexed citations
12.
Zhai, Juan, et al.. (2021). {ELISE}: A Storage Efficient Logging System Powered by Redundancy Reduction and Representation Learning. USENIX Security Symposium. 3023–3040. 4 indexed citations
13.
Lai, Wanchang, et al.. (2020). Based on Differential Evolution Algorithm X Fluorescence Peak Overlapping Decomposition. Guangpuxue yu guangpu fenxi. 40(7). 2301. 1 indexed citations
14.
Zhai, Juan, Yu Shi, Minxue Pan, et al.. (2020). C2S: translating natural language comments to formal program specifications. 25–37. 27 indexed citations
15.
Zhai, Juan, Yu Shi, Guanhong Tao, et al.. (2020). CPC. 1359–1371. 25 indexed citations
16.
Wang, Shen, Jie Li, Yilong Guo, et al.. (2020). Treatment with soluble bone morphogenetic protein type 1A receptor fusion protein alleviates irradiation-induced bone loss in mice through increased bone formation and reduced bone resorption.. PubMed. 12(3). 743–757. 2 indexed citations
17.
Tang, Zhenhao, Juan Zhai, Minxue Pan, et al.. (2018). Dual-force: understanding WebView malware via cross-language forced execution. View. 714–725. 6 indexed citations
18.
Lee, Kyu Hyoung, Xiangyu Zhang, Dongyan Xu, et al.. (2017). MPI: multiple perspective attack investigation with semantics aware execution partitioning. View. 1111–1128. 31 indexed citations
19.
Zhai, Juan. (2010). Biomass Modeling of Different Grassland Types Based on NDVI in Tibet. 3 indexed citations
20.
Zhai, Juan, et al.. (1996). Estrogen Receptor Messenger Ribonucleic Acid Changes during Leydig Cell Development1. Biology of Reproduction. 55(4). 782–788. 39 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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