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 Bin Wu sits on this spectrum.
This scientist: 324 publications — 78th percentile
78% 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 Bin Wu sits on this spectrum.
This scientist: 36 D-Index — 23rd percentile
23% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Bin Wu is affiliated with Beijing University of Posts and Telecommunications in China. Their research output primarily focuses on computer science, with a strong emphasis on artificial intelligence, computer vision and pattern recognition, statistical and nonlinear physics, sociology and political science, and information systems.
The scientist's recent papers reflect an engagement with advanced machine learning techniques applied to complex problems such as fake news detection and population dynamics. Selected recent publications include:
Bin Wu collaborates frequently with several researchers including Jiehu Kang, Luyuan Feng, Chenguang Song, Nianwen Ning, and Zefeng Sun. This collaboration reflects a networked approach to their research, incorporating diverse expertise.
Their publications appear most commonly in venues such as arXiv (Cornell University), Applied Optics, SSRN Electronic Journal, Information Processing & Management, and Neurocomputing. These journals and repositories highlight Bin Wu's interdisciplinary reach and engagement with both theoretical and applied research communities.
Their work spans various topics within computer science and beyond, notably:
This wide range of topics indicates an interest in both computational methods and their applications to understanding social, biological, and physical systems. Bin Wu's research incorporates state-of-the-art neural network architectures, graph-based modeling, and evolutionary dynamics, reflecting cross-disciplinary expertise.
Chuan Shi;Xiangnan Kong;Yue Huang;Philip S. Yu
Nan Du;Bin Wu;Xin Pei;Bai Wang
Chuan Shi;Zhiqiang Zhang;Ping Luo;Philip S. Yu
Chuan Shi;Zhenyu Yan;Yanan Cai;Bin Wu
Chenguang Song;Nianwen Ning;Yunlei Zhang;Bin Wu
Chuan Shi;Yanan Cai;Di Fu;Yuxiao Dong
Yanqing Yang;Kangfeng Zheng;Bin Wu;Yixian Yang
Chenguang Song;Kai Shu;Bin Wu
Chuan Shi;Xiangnan Kong;Philip S. Yu;Sihong Xie
Bin Wu;Cunhua Qian;Weihong Ni;Shuhai Fan
Jian-Guang Lou;Qiang Fu;Shengqi Yang;Jiang Li
Bin Wu;Cunhua Qian;Weihong Ni;Shuhai Fan
Xiuqin Lin;Peng Wang;Bin Wu
Tian Zhu;Bai Wang;Bin Wu;Chuanxi Zhu
Wenzhe Wang;Bin Wu;Sixiong Yang;Zhixiang Wang
Nan Du;Bin Wu;Liutong Xu;Bai Wang
B. Wu;X. Wang;J. Guo;S. Xie
Chuan Shi;Chuan Shi;Zhenyu Yan;Yi Wang;Yanan Cai
Chuan Shi;Jian Liu;Fuzhen Zhuang;Philip S. Yu
Bin Wu;Xin Pei
Nan Du;Bai Wang;Bin Wu;Yi Wang
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