Gang Li

47.6k total citations · 7 hit papers
590 papers, 10.7k citations indexed

About

Gang Li is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Gang Li has authored 590 papers receiving a total of 10.7k indexed citations (citations by other indexed papers that have themselves been cited), including 180 papers in Artificial Intelligence, 111 papers in Computer Vision and Pattern Recognition and 102 papers in Computer Networks and Communications. Recurrent topics in Gang Li's work include Diverse Aspects of Tourism Research (39 papers), Digital Marketing and Social Media (33 papers) and Privacy-Preserving Technologies in Data (31 papers). Gang Li is often cited by papers focused on Diverse Aspects of Tourism Research (39 papers), Digital Marketing and Social Media (33 papers) and Privacy-Preserving Technologies in Data (31 papers). Gang Li collaborates with scholars based in China, Australia and United States. Gang Li's co-authors include Rob Law, Dong Yu, Frank Seide, Huy Quan Vu, Jia Rong, Zeng Meng, Changting Zhong, Wanlei Zhou, Chen Xie and Tianqing Zhu and has published in prestigious journals such as Angewandte Chemie International Edition, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Gang Li

543 papers receiving 10.1k citations

Hit Papers

Conversational speech transcription using context-depende... 2011 2026 2016 2021 2011 2022 2011 2014 2019 100 200 300 400 500

Peers

Gang Li
Comparison fields: 5 of 214
  • Artificial Intelligence 4.1k
  • Sociology and Political Science 2.4k
  • Signal Processing 1.9k
  • Computer Vision and Pattern Recognition 1.4k
  • Information Systems 1.1k
Replace Kevin Lang with:
Kevin Lang United States
Amir Hussain United Kingdom
Wei Wang China
Yan Wang China
Jaideep Srivastava United States
Eric Horvitz United States
Lior Rokach Israel
Yanchun Zhang Australia
Anupam Joshi United States
Foster Provost United States
Kevin Lang United States View profile →
Citations per field, relative to Gang Li
Gang Li · 1×
Citations per year, relative to Gang Li
Gang Li · 1×

Countries citing papers authored by Gang Li

Since Specialization
Citations

This map shows the geographic impact of Gang Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gang Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gang Li more than expected).

Fields of papers citing papers by Gang Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gang Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gang Li. The network helps show where Gang Li may publish in the future.

Co-authorship network of co-authors of Gang Li

This figure shows the co-authorship network connecting the top 25 collaborators of Gang Li. A scholar is included among the top collaborators of Gang Li based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gang Li. Gang Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 5
2 12
3 2
4 17
5 0
6 0
7 0
8 8
9 1
10 2
11 0
12 0
13 3
14 8
15 7
16 3
17 14
18 1
19
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs breakdown →
439
20 16

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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