Zhenyu Liao

937 total citations
43 papers, 410 citations indexed

About

Zhenyu Liao is a scholar working on Artificial Intelligence, Materials Chemistry and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Zhenyu Liao has authored 43 papers receiving a total of 410 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Materials Chemistry and 7 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Zhenyu Liao's work include Ferroelectric and Piezoelectric Materials (7 papers), Multiferroics and related materials (5 papers) and Bayesian Methods and Mixture Models (3 papers). Zhenyu Liao is often cited by papers focused on Ferroelectric and Piezoelectric Materials (7 papers), Multiferroics and related materials (5 papers) and Bayesian Methods and Mixture Models (3 papers). Zhenyu Liao collaborates with scholars based in China, United States and France. Zhenyu Liao's co-authors include Qing Jin, Linjie Yang, Romain Couillet, Jing Zhu, Yueliang Li, Wei Sun, Jing‐Feng Li, Dongsheng Song, Fang Fang and Longtu Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, ACS Nano and Applied Physics Letters.

In The Last Decade

Zhenyu Liao

40 papers receiving 403 citations

Peers

Zhenyu Liao
Comparison fields: 5 of 89
  • Materials Chemistry 132
  • Artificial Intelligence 116
  • Electronic, Optical and Magnetic Materials 104
  • Computer Vision and Pattern Recognition 85
  • Electrical and Electronic Engineering 63
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Jooyoung Choi South Korea View profile →
Citations per field, relative to Zhenyu Liao
Zhenyu Liao · 1×
Citations per year, relative to Zhenyu Liao
Zhenyu Liao · 1×

Countries citing papers authored by Zhenyu Liao

Since Specialization
Citations

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

Fields of papers citing papers by Zhenyu Liao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenyu Liao

This figure shows the co-authorship network connecting the top 25 collaborators of Zhenyu Liao. A scholar is included among the top collaborators of Zhenyu Liao 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 Zhenyu Liao. Zhenyu Liao 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 0
2 1
3 1
4 0
5 1
6 19
7 4
8 1
9 3
10
Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group
9
11 18
12 2
13 40
14 9
15
The Dynamics of Learning: A Random Matrix Approach
0
16 5
17 1
18 43
19 4
20
NUMERICAL SIMULATION OF DECAY HEAT REMOVAL AFTER CEFR SCRAM
2

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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