The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Changshui Zhang sits on this spectrum.
This scientist: 482 publications — 92nd percentile
92% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Changshui Zhang sits on this spectrum.
This scientist: 79 D-Index — 92nd percentile
92% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Changshui Zhang is affiliated with Tsinghua University in China and has contributed extensively to the field of Computer Science, with a particular focus on Artificial Intelligence and Computer Vision and Pattern Recognition.
The researcher has published over two hundred works, with significant attention to subfields such as Signal Processing, Transportation, and Materials Chemistry. Major thematic areas include Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, and Multimodal Machine Learning Applications. Other notable topics of work are Anomaly Detection Techniques and Applications, Topic Modeling, Adversarial Robustness in Machine Learning, and Explainable Artificial Intelligence (XAI).
Frequent publication venues for Zhang's research include:
Changshui Zhang has collaborated regularly with a group of co-authors, including Shan You, Chen Qian, Chang Xu, Fei Wang, and Ruixin Hong.
Several recent papers authored or co-authored by Zhang demonstrate breadth in both applied and theoretical aspects of machine learning and recognition technologies:
In addition to articles, Zhang has authored multiple books published by Springer Science+Business Media, mainly concentrated on Pattern Recognition and Computer Vision. These works, mostly published in 2021, contribute to the knowledge dissemination in the field.
Recognition for professional contributions includes the IEEE Fellow award received in 2018 for work on semi-supervised learning techniques applied to signal processing tasks.
Zhuang Liu;Jianguo Li;Zhiqiang Shen;Gao Huang
Fei Wang;Changshui Zhang
Zhonglin Lin;Changshui Zhang;Wei Wu;Xiaorong Gao
Shiliang Sun;Changshui Zhang;Guoqiang Yu
Shiming Xiang;Feiping Nie;Changshui Zhang
Feiping Nie;Dong Xu;Ivor Wai-Hung Tsang;Changshui Zhang
Shiming Xiang;Feiping Nie;Gaofeng Meng;Chunhong Pan
Ce Liu;Heung-Yeung Shum;Chang-Shui Zhang
Jingrui He;Mingjing Li;Hong-Jiang Zhang;Hanghang Tong
Hanghang Tong;Mingjing Li;Hongjiang Zhang;Changshui Zhang
Feiping Nie;Shiming Xiang;Yangqing Jia;Changshui Zhang
Runpeng Cui;Hu Liu;Changshui Zhang
Jiang Lu;Jie Hu;Guannan Zhao;Fenghua Mei
Junqi Jin;Kun Fu;Changshui Zhang
Runpeng Cui;Hu Liu;Changshui Zhang
Feiping Nie;Zinan Zeng;I. W. Tsang;Dong Xu
Yangqing Jia;Feiping Nie;Changshui Zhang
Pinghua Gong;Jieping Ye;Changshui Zhang
Pinghua Gong;Changshui Zhang;Zhaosong Lu;Jianhua Huang
Hongwei Qin;Xiu Li;Jian Liang;Yigang Peng
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