Yangxin Huang

2.2k citations
117 papers · 1.5k indexed · h-index 21
Topics
Statistical Methods and Bayesian Inference (52 papers)Bayesian Methods and Mixture Models (38 papers)Statistical Methods and Inference (29 papers)
Journals
SHILAP Revista de lepidopterologíaPLoS ONENeurology
Partner nations
United StatesChinaCanada

In The Last Decade

Yangxin Huang

107 papers receiving 1.5k citations

Peers

Yangxin Huang
Comparison fields: 5 of 138
  • Statistics and Probability 542
  • Artificial Intelligence 327
  • Virology 279
  • Infectious Diseases 228
  • Molecular Biology 221
Replace Lang Wu with:
Lang Wu Canada
Victor DeGruttola United States
Wei Tan United States
William B. Capra United States
Jiangang Liao United States
Beat Neuenschwander Switzerland
Cong Han United States
S. H. Heisterkamp Netherlands
Ryung S. Kim United States
Elena S. Izmailova United States
Yangxin Huang relative to Lang Wu Canada Lang Wu's profile →
Citations per field
00.5×4.9×
Lang Wu · 1×
Citations per year

Countries citing papers authored by Yangxin Huang

Since Specialization
Citations

This map shows the geographic impact of Yangxin Huang'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 Yangxin Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yangxin Huang more than expected).

Fields of papers citing papers by Yangxin Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yangxin Huang. 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 Yangxin Huang. The network helps show where Yangxin Huang may publish in the future.

Co-authorship network of co-authors of Yangxin Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Yangxin Huang. A scholar is included among the top collaborators of Yangxin Huang 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 Yangxin Huang. Yangxin Huang 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
#WorkIndexed citations
1 0
2 0
3 1
4 26
5 12
6 10
7 3
8 1
9 8
10 9
11 11
12 3
13 28
14 4
15 41
16 32
17 66
18 169
19 95
20 2

About Yangxin Huang

Yangxin Huang is a scholar working on Statistics and Probability, Virology and Artificial Intelligence, having authored 117 papers that have together received 1.5k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (52 papers), Bayesian Methods and Mixture Models (38 papers) and Statistical Methods and Inference (29 papers). The work is most often cited by research in Statistics and Probability (542 citations), Virology (279 citations) and Modeling and Simulation (64 citations). Yangxin Huang has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Hulin Wu, Getachew Dagne, Dacheng Liu, Susan L. Rosenkranz, Lang Wu, Henian Chen, Grace Y. Yi, Hanze Zhang, Wei Liu and Edward P. Acosta. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Neurology.

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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