Gokul Krishnan
- Electrical and Electronic Engineering
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Cellular and Molecular Neuroscience
- Hardware and Architecture
- Co-authors
- Yu CaoÜmit Y. OgrasJae-sun SeoSumit K. MandalRajiv JoshiChaitali ChakrabartiGouranga CharanNathaniel C. Cady
- Topics
- Advanced Memory and Neural Computing (21 papers)Ferroelectric and Negative Capacitance Devices (17 papers)CCD and CMOS Imaging Sensors (6 papers)
- Cited by
- Hardware and ArchitectureElectrical and Electronic EngineeringComputer Vision and Pattern Recognition
- Journals
- Inorganic ChemistryIEEE Transactions on ComputersIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Partner nations
- United StatesChinaIndia
In The Last Decade
Gokul Krishnan
24 papers receiving 277 citations
Peers
Comparison fields: 5 of 36
- Electrical and Electronic Engineering 239
- Artificial Intelligence 60
- Computer Vision and Pattern Recognition 57
- Cellular and Molecular Neuroscience 31
- Hardware and Architecture 28
Countries citing papers authored by Gokul Krishnan
This map shows the geographic impact of Gokul Krishnan'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 Gokul Krishnan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gokul Krishnan more than expected).
Fields of papers citing papers by Gokul Krishnan
This network shows the impact of papers produced by Gokul Krishnan. 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 Gokul Krishnan. The network helps show where Gokul Krishnan may publish in the future.
Co-authorship network of co-authors of Gokul Krishnan
This figure shows the co-authorship network connecting the top 25 collaborators of Gokul Krishnan. A scholar is included among the top collaborators of Gokul Krishnan 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 Gokul Krishnan. Gokul Krishnan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 20 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 4 | |
| 6 | 8 | |
| 7 | 9 | |
| 8 | 7 | |
| 9 | 11 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 30 | |
| 13 | 30 | |
| 14 | 25 | |
| 15 | 3 | |
| 16 | 53 | |
| 17 | 19 | |
| 18 | 2 | |
| 19 | 4 | |
| 20 | 9 |
About Gokul Krishnan
Gokul Krishnan is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Polymers and Plastics, having authored 26 papers that have together received 280 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (21 papers), Ferroelectric and Negative Capacitance Devices (17 papers) and CCD and CMOS Imaging Sensors (6 papers). The work is most often cited by research in Hardware and Architecture (28 citations), Electrical and Electronic Engineering (239 citations) and Computer Vision and Pattern Recognition (57 citations). Gokul Krishnan has collaborated with scholars based in United States, China and India. Frequent co-authors include Yu Cao, Ümit Y. Ogras, Jae-sun Seo, Sumit K. Mandal, Rajiv Joshi, Chaitali Chakrabarti, Gouranga Charan, Nathaniel C. Cady, Abinash Mohanty and Karsten Beckmann. Their work appears in journals such as Inorganic Chemistry, IEEE Transactions on Computers and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
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.