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 Chuanxiong Guo sits on this spectrum.
This scientist: 111 publications — 12th percentile
12% 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 Chuanxiong Guo sits on this spectrum.
This scientist: 47 D-Index — 56th percentile
56% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Chuanxiong Guo is affiliated with ByteDance in China and specializes in computer science, with a focus on distributed and parallel computing systems. Their research spans multiple subfields, including computer networks and communications, artificial intelligence, computer vision and pattern recognition, information systems, and industrial and manufacturing engineering.
The scientist has contributed to several research topics, particularly in distributed and parallel computing systems, cloud computing and resource management, and IoT and edge/fog computing. Other notable areas of focus include advanced neural network applications, brain tumor detection and classification, advanced graph neural networks, and graph theory and algorithms.
Guo has published papers primarily in the venue arXiv (Cornell University), with additional work appearing in the Leibniz-Zentrum für Informatik (Schloss Dagstuhl). The document identifies seven publications at arXiv and one at Leibniz-Zentrum für Informatik (Schloss Dagstuhl).
Their frequent co-authors include Yibo Zhu with six joint publications, Zherui Liu with three, Chuan Wu and Yanghua Peng with two each, and Jiamin Li with one collaboration.
Guo's research emphasis on optimizing computational resources and scheduling for machine learning models intersects with topics such as GPU-efficient graph neural network training, reconfigurable machine scheduling, and the detection of performance anomalies in RDMA subsystems. Research on elastic cluster schedulers and automatic learning-rate scheduling techniques further illustrates their involvement in advancing resource management in high-performance computing environments.
Chuanxiong Guo;Guohan Lu;Dan Li;Haitao Wu
Chuanxiong Guo;Haitao Wu;Kun Tan;Lei Shi
Chuanxiong Guo;Guohan Lu;Helen J. Wang;Shuang Yang
Yibo Zhu;Haggai Eran;Daniel Firestone;Chuanxiong Guo
Chuanxiong Guo;Haitao Wu;Zhong Deng;Gaurav Soni
Haitao Wu;Zhenqian Feng;Chuanxiong Guo;Yongguang Zhang
Chuanxiong Guo;Lihua Yuan;Dong Xiang;Yingnong Dang
Qian Zhang;Chuanxiong Guo;Zihua Guo;Wenwu Zhu
Helen J. Wang;Chuanxiong Guo;Daniel R. Simon;Alf Zugenmaier
Yanghua Peng;Yixin Bao;Yangrui Chen;Chuan Wu
Yanghua Peng;Yibo Zhu;Yangrui Chen;Yixin Bao
Chuanxiong Guo;Zihua Guo;Qian Zhang;Wenwu Zhu
D. Li;C. Guo;H. Wu;K. Tan
Haitao Wu;Zhenqian Feng;Chuanxiong Guo;Yongguang Zhang
Chuanxiong Guo;Guohan Lv;Shuang Yang;Jiahe Helen Wang
Suk-Bok Lee;Sai-Wang Tam;Ioannis Pefkianakis;Songwu Lu
Haitao Wu;Guohan Lu;Dan Li;Chuanxiong Guo
Guohan Lu;Chuanxiong Guo;Yulong Li;Zhiqiang Zhou
Jiaxin Cao;Rui Xia;Pengkun Yang;Chuanxiong Guo
Juncheng Gu;Mosharaf Chowdhury;Kang G. Shin;Yibo Zhu
Yimin Jiang;Yibo Zhu;Chang Lan;Bairen Yi
Chuanxiong Guo;Zihua Guo;Qian Zhang;Wenwu Zhu
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