Quan Long

831 citations
23 papers · 517 indexed · h-index 12
Topics
Probabilistic and Robust Engineering Design (6 papers)Optimal Experimental Design Methods (5 papers)Advanced Multi-Objective Optimization Algorithms (4 papers)

In The Last Decade

Quan Long

22 papers receiving 498 citations

Peers

Quan Long
Comparison fields: 5 of 91
  • Statistics, Probability and Uncertainty 167
  • Computational Theory and Mathematics 130
  • Management Science and Operations Research 109
  • Computational Mechanics 94
  • Artificial Intelligence 79
Replace Dipankar Ghosh with:
Dipankar Ghosh United States
Xuchen Han United States
Bret A. Naylor United States
Anoop Mullur United States
Shawn Gano United States
Wolfgang Betz Germany
P. W. Aitchison Canada
Liming Zhang China
Loïc Brevault France
Yongdao Zhou China
Quan Long relative to Dipankar Ghosh United States Dipankar Ghosh's profile →
Citations per field
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Dipankar Ghosh · 1×
Citations per year

Countries citing papers authored by Quan Long

Since Specialization
Citations

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

Fields of papers citing papers by Quan Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Quan Long

This figure shows the co-authorship network connecting the top 25 collaborators of Quan Long. A scholar is included among the top collaborators of Quan Long 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 Quan Long. Quan Long 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 4
2 1
3 1
4 4
5 1
6 5
7 10
8 7
9 52
10 16
11 18
12 33
13 30
14 88
15 43
16 60
17 5
18 55
19 24
20 24

About Quan Long

Quan Long is a scholar working on Statistics, Probability and Uncertainty, Software and Management Science and Operations Research, having authored 23 papers that have together received 517 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (6 papers), Optimal Experimental Design Methods (5 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (167 citations), Software (59 citations) and Computer Graphics and Computer-Aided Design (35 citations). Quan Long has collaborated with scholars based in United States, China and Saudi Arabia. Frequent co-authors include Fehmi Cirak, Raúl Tempone, Suojin Wang, Min Xie, Yuan-Shun Dai, Luis Espath, Joakim Beck, Mohammad Motamed, Serge Prudhomme and Kaushik Bhattacharya. Their work appears in journals such as Applied Physics Letters, Chemical Engineering Journal and Chemosphere.

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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