Ligang Gao
- Electrical and Electronic Engineering top 5%
- Cellular and Molecular Neuroscience top 5%
- Polymers and Plastics top 10%
- Artificial Intelligence top 10%
- Cognitive Neuroscience top 10%
- Co-authors
- Shimeng YuPai-Yu ChenDmitri B. StrukovFabien AlibartWenhao ChenRunchen FangWeijie YuI‐Ting Wang
- Topics
- Advanced Memory and Neural Computing (23 papers)Ferroelectric and Negative Capacitance Devices (12 papers)Neuroscience and Neural Engineering (10 papers)
- Cited by
- Cellular and Molecular NeuroscienceElectrical and Electronic EngineeringPolymers and Plastics
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Ligang Gao
33 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 51
- Electrical and Electronic Engineering 1.1k
- Cellular and Molecular Neuroscience 420
- Polymers and Plastics 162
- Artificial Intelligence 144
- Cognitive Neuroscience 133
Countries citing papers authored by Ligang Gao
This map shows the geographic impact of Ligang Gao'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 Ligang Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ligang Gao more than expected).
Fields of papers citing papers by Ligang Gao
This network shows the impact of papers produced by Ligang Gao. 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 Ligang Gao. The network helps show where Ligang Gao may publish in the future.
Co-authorship network of co-authors of Ligang Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Ligang Gao. A scholar is included among the top collaborators of Ligang Gao 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 Ligang Gao. Ligang Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 1 | |
| 3 | 129 | |
| 4 | 6 | |
| 5 | 12 | |
| 6 | 55 | |
| 7 | 8 | |
| 8 | 119 | |
| 9 | 39 | |
| 10 | 52 | |
| 11 | 92 | |
| 12 | 132 | |
| 13 | 15 | |
| 14 | 18 | |
| 15 | 22 | |
| 16 | 69 | |
| 17 | 3 | |
| 18 | 8 | |
| 19 | 4 | |
| 20 | 9 |
About Ligang Gao
Ligang Gao is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Instrumentation, having authored 33 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (23 papers), Ferroelectric and Negative Capacitance Devices (12 papers) and Neuroscience and Neural Engineering (10 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (420 citations), Electrical and Electronic Engineering (1.1k citations) and Polymers and Plastics (162 citations). Ligang Gao has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Shimeng Yu, Pai-Yu Chen, Dmitri B. Strukov, Fabien Alibart, Wenhao Chen, Runchen Fang, Weijie Yu, I‐Ting Wang, Rui Liu and Sarma Vrudhula. Their work appears in journals such as Physical Review Letters, Applied Physics Letters and Journal of Applied Physics.
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.