Ming Ouyang

26 papers receiving 542 citations

Peers

Ming Ouyang
Comparison fields: 5 of 109
  • Developmental Neuroscience 42
  • Statistics and Probability 39
  • Artificial Intelligence 133
  • Molecular Biology 275
  • Computer Vision and Pattern Recognition 75
Replace Cong Lei with:
Cong Lei China
Jieting Wang China
Florence d’Alché–Buc France
Zhizheng Zhang China
Hyong Kim United States
Xiaoxin Guo China
Chenxi Sun China
Vanathi Gopalakrishnan United States
KiYoung Lee South Korea
Ming Ouyang relative to Cong Lei China Cong Lei's profile →
Citations per field
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Citations per year

Countries citing papers authored by Ming Ouyang

Since Specialization
Citations

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

Fields of papers citing papers by Ming Ouyang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004129
2 200593
3 200777
4 200953
5 200825
6
Hierarchical Clustering with CUDA/GPU.
200923
7 200820
8 201020
9 200718
10 200717
11 200117
12 200316
13 201712
14 19987
15
AES and DES Encryption with GPU
20096
16 20165
17 20184
18 20184
19 19983
20 20033

About Ming Ouyang

Ming Ouyang is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability and Computational Theory and Mathematics, having authored 31 papers that have together received 568 indexed citations. Recurring topics across this work include Gene expression and cancer classification (8 papers), Statistical Methods and Inference (5 papers), Bayesian Methods and Mixture Models (4 papers), Bioinformatics and Genomic Networks (4 papers), Statistical Methods and Bayesian Inference (3 papers), Complexity and Algorithms in Graphs (3 papers), Data Management and Algorithms (2 papers) and Graph Theory and Algorithms (2 papers). The work is most often cited by research in Developmental Neuroscience (42 citations), Statistics and Probability (39 citations), Artificial Intelligence (133 citations), Molecular Biology (275 citations) and Computer Vision and Pattern Recognition (75 citations). Ming Ouyang has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include William J. Welsh, Panos G. Georgopoulos, Rebecka Jörnsten, Eric C. Rouchka, Yu R. Han, Jiadong Li, Patrizia Casaccia‐Bonnefil, Dongming Sun, Mark R. Plummer and Andrew Y. S. Cheng. Their work appears in journals such as Bioinformatics, Structural Equation Modeling A Multidisciplinary Journal, Journal of Neuroscience, Statistics in Medicine and Academic Emergency Medicine.

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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