Cao Gao
- Computer Networks and Communications top 2%
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering top 10%
- Information Systems top 5%
- Co-authors
- Trevor MudgeJohann HauswaldAustin RovinskiLingjia TangYiping KangJason MarsRonald DreslinskiAnthony Gutierrez
- Topics
- IoT and Edge/Fog Computing (4 papers)Advanced Neural Network Applications (4 papers)Advanced Memory and Neural Computing (3 papers)
- Cited by
- Computer Networks and CommunicationsComputer Vision and Pattern RecognitionArtificial Intelligence
- Journals
- ACM SIGPLAN NoticesACM SIGOPS Operating Systems ReviewACM SIGARCH Computer Architecture News
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Cao Gao
7 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 60
- Computer Networks and Communications 720
- Computer Vision and Pattern Recognition 628
- Artificial Intelligence 378
- Electrical and Electronic Engineering 357
- Information Systems 128
Countries citing papers authored by Cao Gao
This map shows the geographic impact of Cao 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 Cao Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cao Gao more than expected).
Fields of papers citing papers by Cao Gao
This network shows the impact of papers produced by Cao 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 Cao Gao. The network helps show where Cao Gao may publish in the future.
Co-authorship network of co-authors of Cao Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Cao Gao. A scholar is included among the top collaborators of Cao 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 Cao Gao. Cao 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 | 111 | |
| 2 | Neurosurgeonbreakdown → | 449 |
| 3 | 51 | |
| 4 | Neurosurgeonbreakdown → | 527 |
| 5 | 17 | |
| 6 | 44 | |
| 7 | 15 |
About Cao Gao
Cao Gao is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 7 papers that have together received 1.2k indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (4 papers), Advanced Neural Network Applications (4 papers) and Advanced Memory and Neural Computing (3 papers). The work is most often cited by research in Computer Networks and Communications (720 citations), Computer Vision and Pattern Recognition (628 citations) and Artificial Intelligence (378 citations). Cao Gao has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Trevor Mudge, Johann Hauswald, Austin Rovinski, Lingjia Tang, Yiping Kang, Jason Mars, Ronald Dreslinski, Anthony Gutierrez, Carole-Jean Wu and Jingcheng Wang. Their work appears in journals such as ACM SIGPLAN Notices, ACM SIGOPS Operating Systems Review and ACM SIGARCH Computer Architecture News.
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.