The chart shows the distribution of publications by all Research.com ranked scientists in the field of Chemistry in 2026. The highlighted bar marks where Qing Wu sits on this spectrum.
This scientist: 201 publications — 33rd percentile
33% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 1,295 publications or more.
The chart shows the D-index (discipline H-index) distribution of Chemistry scientists ranked by Research.com in 2026. The highlighted bar marks where Qing Wu sits on this spectrum.
This scientist: 44 D-Index — 7th percentile
7% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 159 D-Index or more.
Qing Wu is affiliated with Sun Yat-sen University in China, focusing on research primarily in computer science and environmental science. Their scholarly contributions span various interconnected fields including artificial intelligence, industrial and manufacturing engineering, computer networks and communications, environmental engineering, and water science and technology.
The scientist's work covers several key topics such as explainable artificial intelligence (XAI), Gaussian processes and Bayesian inference, machine learning and data classification, air quality monitoring and forecasting, water quality monitoring and analysis, water quality monitoring technologies, and neural networks stability and synchronization.
Qing Wu has authored multiple papers in notable publication venues. These include:
The scientist collaborates with a range of peers, frequently co-authoring with Andrea Saltelli, Anthony J. Jakeman, Saman Razavi, Chunxue Wu, and Cheng Wang, each contributing to the interdisciplinary nature of their research output.
Publication venues Qing Wu has contributed to reflect the interdisciplinary focus of their work:
Qing Wu's research integrates computational techniques with environmental science applications, particularly in monitoring and analyzing air and water quality. The application of advanced machine learning models and explainable AI approaches indicates an emphasis on developing interpretable and data-driven environmental monitoring systems.
Feng-Shou Liu;Hai-Bin Hu;Ying Xu;Li-Hua Guo
Liu Zhong;Guiliang Li;Guodong Liang;Haiyang Gao
Lihua Guo;Lihua Guo;Haiyang Gao;Qirui Guan;Haibin Hu
Xiaoying Wang;Kairong Liao;Daping Quan;Qing Wu
Jun Liu;Darui Chen;Han Wu;Zefan Xiao
Shuhuang Zhong;Yingxin Tan;Liu Zhong;Jie Gao
Haiyang Gao;Wenjuan Guo;Feng Bao;Guoqiu Gui
Haiyang Gao;Haibin Hu;Fangming Zhu;Qing Wu
Lixia Pei;Lixia Pei;Fengshou Liu;Heng Liao;Jie Gao
Li-hua Guo;Hai-yang Gao;Ling Zhang;Fang-ming Zhu
Ting Li;Wan Juan Wang;Ran Liu;Wei Hao Liang
Shaobo Zai;Haiyang Gao;Zengfang Huang;Haibin Hu
Haibin Hu;Haiyang Gao;Darui Chen;Guiliang Li
Hao Zou;Fang Ming Zhu;Qing Wu;Jiao Yan Ai
Qing Wu;Yingying Lu
Shaobo Zai;Fengshou Liu;Haiyang Gao;Cong Li
Haibin Hu;Lei Zhang;Haiyang Gao;Fangming Zhu
Keming Song;Haiyang Gao;Fengshou Liu;Jin Pan
Xinbo Shi;Ye Zhao;Haiyang Gao;Ling Zhang
Haiyang Gao;Xiaofang Liu;Ying Tang;Jin Pan
Haiyang Gao;Yan Chen;Fangming Zhu;Qing Wu
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