Mingxuan Yuan

2.1k citations
100 papers · 1.2k indexed · h-index 18

Mingxuan Yuan

89 papers receiving 1.1k citations

Peers

Mingxuan Yuan
Comparison fields: 5 of 90
  • Computational Mathematics 13
  • Transportation 140
  • Industrial and Manufacturing Engineering 169
  • Artificial Intelligence 457
  • Signal Processing 149
Replace Konstantinos Pelechrinis with:
Konstantinos Pelechrinis United States
Wei Cao China
Yongrui Qin United Kingdom
Xiaoxian Yang China
Chunhua Hu China
André Luckow United States
Asma Belhadi Norway
Wen-Chih Peng Taiwan
Abdelkarim Erradi Qatar
Jianling Sun China
Mingxuan Yuan relative to Konstantinos Pelechrinis United States Konstantinos Pelechrinis's profile →
Citations per field
00.5×4.2×
Konstantinos Pelechrinis · 1×
Citations per year

Countries citing papers authored by Mingxuan Yuan

Since Specialization
Citations

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

Fields of papers citing papers by Mingxuan Yuan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Mingxuan Yuan, 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 Mingxuan Yuan Line = papers co-authored together Mingxuan Yuan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20260
2 20250
3 20251
4 20250
5 20241
6 20243
7 20240
8 20240
9 20248
10 20241
11 202311
12 20232
13 20232
14 20227
15 202148
16
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems
202139
17 202126
18 20181
19
Discover the Misinformation Broadcasting in On-Line Social Networks
20151
20 20141

About Mingxuan Yuan

Mingxuan Yuan is a scholar working on Hardware and Architecture, Transportation and Computational Mathematics, having authored 100 papers that have together received 1.2k indexed citations. Recurring topics across this work include Human Mobility and Location-Based Analysis (13 papers), Data Management and Algorithms (11 papers), VLSI and FPGA Design Techniques (10 papers), Indoor and Outdoor Localization Technologies (8 papers), Advanced Manufacturing and Logistics Optimization (8 papers), VLSI and Analog Circuit Testing (8 papers), Optimization and Packing Problems (8 papers) and Privacy-Preserving Technologies in Data (7 papers). The work is most often cited by research in Computational Mathematics (13 citations), Transportation (140 citations) and Industrial and Manufacturing Engineering (169 citations). Mingxuan Yuan has collaborated with scholars based in China, Hong Kong and Sweden. Frequent co-authors include Lei Chen, Jia Zeng, Philip S. Yu, Ting Yu, Hui‐Ling Zhen, Qiang Yang, Jianguo Yao, Yanhua Li, Weixiong Rao and Jia Zeng. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Proceedings of the VLDB Endowment, IEEE Transactions on Mobile Computing, Aerospace Science and Technology and IEEE Transactions on Big Data.

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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