Chao Wu

4.1k total citations · 1 hit paper
97 papers, 2.4k citations indexed

About

Chao Wu is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chao Wu has authored 97 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Artificial Intelligence, 24 papers in Information Systems and 24 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chao Wu's work include Privacy-Preserving Technologies in Data (15 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Web Data Mining and Analysis (8 papers). Chao Wu is often cited by papers focused on Privacy-Preserving Technologies in Data (15 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Web Data Mining and Analysis (8 papers). Chao Wu collaborates with scholars based in China, United Kingdom and United States. Chao Wu's co-authors include Yike Guo, Hao Dong, Akara Supratak, Simiao Yu, Paul M. Matthews, Wei Pan, Simon Hu, Jun Xiao, Xiaoxiang Na and Julien Lépine and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Cleaner Production.

In The Last Decade

Chao Wu

90 papers receiving 2.3k 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.

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

2017 846 citations Akara Supratak, Hao Dong et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chao Wu China	21	980	516	368	359	297	97	2.4k
Miad Faezipour United States	28	763 0.8×	775 1.5×	260 0.7×	300 0.8×	334 1.1×	145	3.0k
Enas Abdulhay Jordan	22	772 0.8×	316 0.6×	175 0.5×	292 0.8×	71 0.2×	49	2.1k
Khaled Elleithy United States	31	449 0.5×	513 1.0×	110 0.3×	931 2.6×	198 0.7×	244	3.0k
Xiangwei Zheng China	21	539 0.6×	416 0.8×	449 1.2×	240 0.7×	43 0.1×	136	1.6k
Mehmet Bayğın Türkiye	26	619 0.6×	399 0.8×	210 0.6×	374 1.0×	73 0.2×	111	2.2k
Matjaž Gams Slovenia	25	260 0.3×	522 1.0×	407 1.1×	776 2.2×	62 0.2×	166	2.6k
Zhongmin Wang China	25	462 0.5×	424 0.8×	495 1.3×	659 1.8×	29 0.1×	132	2.6k
Teddy Surya Gunawan Malaysia	23	164 0.2×	706 1.4×	269 0.7×	477 1.3×	44 0.1×	283	2.6k
Syed Umar Amin Saudi Arabia	24	1.2k 1.3×	585 1.1×	129 0.4×	336 0.9×	17 0.1×	47	2.8k
Sonya Coleman United Kingdom	22	367 0.4×	410 0.8×	132 0.4×	999 2.8×	34 0.1×	240	2.6k

Countries citing papers authored by Chao Wu

Since Specialization

Citations

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

Fields of papers citing papers by Chao Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chao Wu

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

Wu, Chao, et al.. (2025). PatchTST-DME: Frequency-Aware Transformer for Potato Price Forecasting in Agricultural Management. Potato Research. 68(4). 3973–3996. 2 indexed citations

2.

Zhang, Ruiyuan, Jiaxiang Liu, Zexi Li, et al.. (2024). Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers. Proceedings of the AAAI Conference on Artificial Intelligence. 38(7). 7269–7277. 1 indexed citations

3.

Wu, Chao, et al.. (2024). A Federated Learning Framework via Decentralized Data Valuation for Chronic Disease Healthcare. 60–65. 1 indexed citations

4.

Zhang, Fengda, Kun Kuang, Long Chen, et al.. (2023). Federated unsupervised representation learning. Frontiers of Information Technology & Electronic Engineering. 24(8). 1181–1193. 43 indexed citations

5.

Li, Yong, et al.. (2023). Formation Outlier Formation Transformation Based on Virtual Robots. 5121–5125. 2 indexed citations

6.

Shen, Tao, Jie Zhang, Fengda Zhang, et al.. (2023). Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives. Frontiers of Information Technology & Electronic Engineering. 24(10). 1390–1402. 23 indexed citations

7.

Wu, Chao, et al.. (2023). UAV Path Planning Based on Vision. 4. 7–10.

8.

Zhang, Xingliang, et al.. (2023). A Multi-Source Information Attention Network for Short Sequential Location Prediction. 42–46.

9.

Zhang, Jie, Bo Li, Chen Chen, et al.. (2023). Delving into the Adversarial Robustness of Federated Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 11245–11253. 27 indexed citations

10.

Wang, Bin, Gang Li, Chao Wu, et al.. (2022). A framework for self-supervised federated domain adaptation. EURASIP Journal on Wireless Communications and Networking. 2022(1). 6 indexed citations

11.

Li, Jinhua, Jun Jiang, Jiezhe Yang, et al.. (2022). Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation. Frontiers in Public Health. 10. 967681–967681. 22 indexed citations

12.

Wu, Chao, et al.. (2021). Design of Deep Learning Model for Task‐Evoked fMRI Data Classification. Computational Intelligence and Neuroscience. 2021(1). 6660866–6660866. 18 indexed citations

13.

Li, Peng, et al.. (2021). Differential Privacy Stochastic Gradient Descent with Adaptive Privacy Budget Allocation. 227–231. 6 indexed citations

14.

Wang, Shuo, Liuqing Chen, Chao Wu, et al.. (2020). Neurocognition-inspired design with machine learning. Design Science. 6. 14 indexed citations

15.

Wu, Chao, Simon Hu, Julien Lépine, et al.. (2020). An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data. Sensors. 20(19). 5564–5564. 101 indexed citations

16.

Yu, Simiao, et al.. (2019). SIMGAN: Photo-Realistic Semantic Image Manipulation Using Generative Adversarial Networks. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 734–738. 7 indexed citations

17.

Zhu, Jiangcheng, Zhepei Wang, Douglas McIlwraith, et al.. (2019). Time‐in‐action RL. SHILAP Revista de lepidopterología. 1(1). 28–37. 1 indexed citations

18.

Wei, Zhen, et al.. (2018). Using Support Vector Machine on EEG for Advertisement Impact Assessment. Frontiers in Neuroscience. 12. 76–76. 59 indexed citations

19.

Dong, Hao, Simiao Yu, Chao Wu, & Yike Guo. (2017). Semantic Image Synthesis via Adversarial Learning. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5707–5715. 153 indexed citations

20.

Wu, Chao, et al.. (2011). TAGS ARE RELATED: MEASUREMENT OF SEMANTIC RELATEDNESS BASED ON FOLKSONOMY NETWORK. Computing and Informatics / Computers and Artificial Intelligence. 30(1). 165–185. 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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