Shuhui Qu

981 total citations
28 papers, 615 citations indexed

About

Shuhui Qu is a scholar working on Artificial Intelligence, Industrial and Manufacturing Engineering and Molecular Biology. According to data from OpenAlex, Shuhui Qu has authored 28 papers receiving a total of 615 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 11 papers in Industrial and Manufacturing Engineering and 3 papers in Molecular Biology. Recurrent topics in Shuhui Qu's work include Scheduling and Optimization Algorithms (8 papers), Anomaly Detection Techniques and Applications (4 papers) and Speech and Audio Processing (3 papers). Shuhui Qu is often cited by papers focused on Scheduling and Optimization Algorithms (8 papers), Anomaly Detection Techniques and Applications (4 papers) and Speech and Audio Processing (3 papers). Shuhui Qu collaborates with scholars based in United States, China and Macao. Shuhui Qu's co-authors include Juncheng Li, Samarjit Das, Wei Dai, Florian Metze, Wei Dai, Tianshu Chu, Jie Wang, James O. Leckie, Fanhong Zeng and Jie Wang and has published in prestigious journals such as Free Radical Biology and Medicine, IEEE Transactions on Power Systems and Frontiers in Pharmacology.

In The Last Decade

Shuhui Qu

27 papers receiving 592 citations

Peers

Shuhui Qu
Comparison fields: 5 of 101
  • Signal Processing 231
  • Artificial Intelligence 134
  • Industrial and Manufacturing Engineering 128
  • Computer Vision and Pattern Recognition 95
  • Control and Systems Engineering 73
Replace Dong‐Hyun Lee with:
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Dong‐Hyun Lee South Korea View profile →
Citations per field, relative to Shuhui Qu
Shuhui Qu · 1×
Citations per year, relative to Shuhui Qu
Shuhui Qu · 1×

Countries citing papers authored by Shuhui Qu

Since Specialization
Citations

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

Fields of papers citing papers by Shuhui Qu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shuhui Qu

This figure shows the co-authorship network connecting the top 25 collaborators of Shuhui Qu. A scholar is included among the top collaborators of Shuhui Qu 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 Shuhui Qu. Shuhui Qu 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 2
2 1
3 4
4 3
5 18
6 32
7 3
8 46
9 9
10 7
11
Real-Time Decision Support with Reinforcement Learning for Dynamic Flowshop Scheduling
9
12 214
13 102
14 27
15 20
16 25
17
An Active Learning-Based Framework for Optimal Product Design Under Multiple Objectives
1
18 8
19 2
20 0

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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