Long Feng

981 citations
81 papers · 681 · h-index 15

Impact in

Papers in

Long Feng

68 papers receiving 676 citations

Peers

Long Feng
Comparison fields: 5 of 70
  • Statistics and Probability 212
  • Mechanical Engineering 235
  • Ecological Modeling 23
  • Statistics, Probability and Uncertainty 34
  • Ceramics and Composites 25
Replace R. Natarajan with:
R. Natarajan India
Fanchao Kong China
Martin Möser Germany
Kai Hu China
Ali Algarni Saudi Arabia
M. Cacciari Italy
Yifan Hu China
Serguei Maximov Mexico
Ashwin Rai United States
Long Feng relative to R. Natarajan India R. Natarajan's profile →
Citations per field
00.5×6.8×
R. Natarajan · 1×
Citations per year

Countries citing papers authored by Long Feng

Since Specialization
Citations

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

Fields of papers citing papers by Long Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Long Feng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Long Feng Line = papers co-authored together Long Feng links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011111
2 201269
3 201434
4 201333
5 201533
6 201631
7 201524
8 202122
9 202022
10 201619
11 201117
12 201317
13 202217
14 201814
15 201414
16 201114
17 202410
18 201510
19 202210
20 201710

About Long Feng

Long Feng is a scholar working on Statistics and Probability, Mechanical Engineering, Electrical and Electronic Engineering, Artificial Intelligence and Control and Systems Engineering, having authored 81 papers that have together received 681 indexed citations. Recurring topics across this work include Statistical Methods and Inference (22 papers), Advanced Statistical Methods and Models (14 papers), earthquake and tectonic studies (7 papers), Spatial and Panel Data Analysis (7 papers), Fault Detection and Control Systems (6 papers), Random Matrices and Applications (6 papers), Fluid Dynamics and Heat Transfer (5 papers) and ZnO doping and properties (5 papers). The work is most often cited by research in Statistics and Probability (212 citations), Mechanical Engineering (235 citations), Ecological Modeling (23 citations), Statistics, Probability and Uncertainty (34 citations) and Ceramics and Composites (25 citations). Long Feng has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Changliang Zou, Zhaojun Wang, L.C. Tsao, Tung‐Han Chuang, Shih‐Ying Chang, H.K. Lin, Lina Yao, Lixing Zhu, Lina Yao and Jiwen Liu. Their work appears in journals such as Journal of Business and Economic Statistics, Statistica Sinica, Journal of Multivariate Analysis, Journal of Econometrics and Electronic Journal of Statistics.

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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