Priyadarshini Panda
- Electrical and Electronic Engineering top 1%
- Artificial Intelligence top 1%
- Cognitive Neuroscience top 1%
- Cellular and Molecular Neuroscience top 2%
- Computer Vision and Pattern Recognition top 2%
- Co-authors
- Kaushik RoyAkhilesh JaiswalGopalakrishnan SrinivasanChankyu LeeYoungeun KimAbhronil SenguptaSyed Shakib SarwarAayush Ankit
- Topics
- Advanced Memory and Neural Computing (64 papers)Ferroelectric and Negative Capacitance Devices (45 papers)Neural dynamics and brain function (28 papers)
- Partner nations
- United StatesIndiaLebanon
In The Last Decade
Priyadarshini Panda
82 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 120
- Electrical and Electronic Engineering 2.8k
- Artificial Intelligence 1.4k
- Cognitive Neuroscience 1.3k
- Cellular and Molecular Neuroscience 685
- Computer Vision and Pattern Recognition 402
Countries citing papers authored by Priyadarshini Panda
This map shows the geographic impact of Priyadarshini Panda'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 Priyadarshini Panda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Priyadarshini Panda more than expected).
Fields of papers citing papers by Priyadarshini Panda
This network shows the impact of papers produced by Priyadarshini Panda. 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 Priyadarshini Panda. The network helps show where Priyadarshini Panda may publish in the future.
Co-authorship network of co-authors of Priyadarshini Panda
This figure shows the co-authorship network connecting the top 25 collaborators of Priyadarshini Panda. A scholar is included among the top collaborators of Priyadarshini Panda 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 Priyadarshini Panda. Priyadarshini Panda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 13 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 1 | |
| 10 | 6 | |
| 11 | 5 | |
| 12 | 1 | |
| 13 | 9 | |
| 14 | 36 | |
| 15 | 7 | |
| 16 | 106 | |
| 17 | 22 | |
| 18 | 22 | |
| 19 | 12 | |
| 20 | 0 |
About Priyadarshini Panda
Priyadarshini Panda is a scholar working on Computational Mathematics, Cognitive Neuroscience and Artificial Intelligence, having authored 92 papers that have together received 3.6k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (64 papers), Ferroelectric and Negative Capacitance Devices (45 papers) and Neural dynamics and brain function (28 papers). The work is most often cited by research in Cognitive Neuroscience (1.3k citations), Electrical and Electronic Engineering (2.8k citations) and Artificial Intelligence (1.4k citations). Priyadarshini Panda has collaborated with scholars based in United States, India and Lebanon. Frequent co-authors include Kaushik Roy, Akhilesh Jaiswal, Gopalakrishnan Srinivasan, Chankyu Lee, Youngeun Kim, Youngeun Kim, Abhronil Sengupta, Syed Shakib Sarwar, Aayush Ankit and Sungeun Hong. Their work appears in journals such as Nature, Nature Communications and Scientific Reports.
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.