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 Jintao Li sits on this spectrum.
This scientist: 241 publications — 60th percentile
60% 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 Jintao Li sits on this spectrum.
This scientist: 40 D-Index — 37th percentile
37% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Jintao Li is affiliated with the Chinese Academy of Sciences in China and has contributed extensively to the field of engineering, with a particular focus on computer vision and pattern recognition, soil science, artificial intelligence, atmospheric science, and ecology. Their research spans multiple interdisciplinary domains within these broader fields.
Li's work covers a diverse range of main topics including soil carbon and nitrogen dynamics, climate change and permafrost, AI in cancer detection, advanced electron microscopy techniques and applications, radiomics and machine learning in medical imaging, soil and unsaturated flow, as well as microbial community ecology and physiology.
Among recent publications by Jintao Li are:
Frequent collaborators in Li's research include Fa Zhang, Ming Nie, Jinquan Li, Shurong Zhou, and Rui Yan. These collaborations have supported work across various interdisciplinary topics and publication venues.
Li has published in several venues multiple times, with notable frequent publication outlets including Zenodo (CERN European Organization for Nuclear Research), arXiv (Cornell University), Global Change Biology, Soil Biology and Biochemistry, and Applied Thermal Engineering.
The broad scope of Li's research reflects engagement with both theoretical and applied aspects of engineering and environmental science, integrating machine learning techniques, medical imaging, and soil and atmospheric studies. This multidisciplinary approach informs their contributions to understanding environmental dynamics as well as computational methods for classification and adaptation in biological systems.
Ji Wan;Dayong Wang;Steven Chu Hong Hoi;Pengcheng Wu
Juan Cao;Tian Xia;Jintao Li;Yongdong Zhang
Ju Sun;Xiao Wu;Shuicheng Yan;Loong-Fah Cheong
Peng Qi;Juan Cao;Tianyun Yang;Junbo Guo
Hantao Yao;Shiliang Zhang;Yongdong Zhang;Jintao Li
Yu Li;Tao Wang;Bingyi Kang;Sheng Tang
Han Guo;Juan Cao;Yazi Zhang;Junbo Guo
Rui Zhang;Sheng Tang;Yongdong Zhang;Jintao Li
Qiong Nan;Juan Cao;Yongchun Zhu;Yanyan Wang
Bo Wu;Wen-Huang Cheng;Yongdong Zhang;Qiushi Huang
Juan Cao;Peng Qi;Qiang Sheng;Tianyun Yang
Xingyu Gao;Zhenyu Chen;Sheng Tang;Yongdong Zhang
Xingyu Gao;Steven C. H. Hoi;Yongdong Zhang;Ji Wan
Xishan Zhang;Jia Jia;Ke Gao;Yongdong Zhang
Hantao Yao;Shiliang Zhang;Yongdong Zhang;Jintao Li
Hongtao Xie;Yongdong Zhang;Jianlong Tan;Li Guo
Juan Cao;Junbo Guo;Xirong Li;Zhiwei Jin
Yu Li;Sheng Tang;Rui Zhang;Yongdong Zhang
Rui Yan;Fa Zhang;Xiaosong Rao;Zhilong Lv
Xishan Zhang;Ke Gao;Yongdong Zhang;Dongming Zhang
Lei Bao;Cao Juan;Jintao Li;Yongdong Zhang
Xishan Zhang;Hanwang Zhang;Yongdong Zhang;Yang Yang
Ronggang Wang;Mo Li;Jintao Li;Yongdong Zhang
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