Lan Huang

496 total citations
22 papers, 348 citations indexed

About

Lan Huang is a scholar working on Statistics and Probability, Toxicology and Artificial Intelligence. According to data from OpenAlex, Lan Huang has authored 22 papers receiving a total of 348 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Statistics and Probability, 8 papers in Toxicology and 4 papers in Artificial Intelligence. Recurrent topics in Lan Huang's work include Statistical Methods in Clinical Trials (10 papers), Pharmacovigilance and Adverse Drug Reactions (8 papers) and Statistical Methods and Inference (4 papers). Lan Huang is often cited by papers focused on Statistical Methods in Clinical Trials (10 papers), Pharmacovigilance and Adverse Drug Reactions (8 papers) and Statistical Methods and Inference (4 papers). Lan Huang collaborates with scholars based in United States and China. Lan Huang's co-authors include Ram C. Tiwari, Jyoti Zalkikar, Joseph G. Ibrahim, Ming‐Hui Chen, Sungduk Kim, Ming‐Hui Chen, Na Hu, Binbing Yu, Pulak Ghosh and Zhihao Yao and has published in prestigious journals such as Journal of the American Statistical Association, Biometrics and Statistics in Medicine.

In The Last Decade

Lan Huang

20 papers receiving 339 citations

Peers

Lan Huang
Comparison fields: 5 of 87
  • Statistics and Probability 202
  • Toxicology 157
  • Computational Theory and Mathematics 92
  • Artificial Intelligence 54
  • Epidemiology 35
Replace Jyoti Zalkikar with:
Jyoti Zalkikar United States
Ivan Zorych United States
Stephanie J. Reisinger United States
Johan Hopstadius Sweden
Joseph M. Tonning United States
Lester Reich United States
Lingling Li United States
Tomas Bergvall Sweden
Gregory Powell United States
Krystl Haerian United States
Jyoti Zalkikar United States View profile →
Citations per field, relative to Lan Huang
Lan Huang · 1×
Citations per year, relative to Lan Huang
Lan Huang · 1×

Countries citing papers authored by Lan Huang

Since Specialization
Citations

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

Fields of papers citing papers by Lan Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lan Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Lan Huang. A scholar is included among the top collaborators of Lan 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 Lan Huang. Lan 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
# Work Indexed citations
1 3
2 0
3 1
4 2
5 6
6 3
7 0
8 5
9 2
10 2
11 2
12 2
13 3
14 23
15 19
16 34
17 65
18 10
19 38
20 35

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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