Vaibhav Karkare
- Electrical and Electronic Engineering
- Cellular and Molecular Neuroscience top 10%
- Cognitive Neuroscience top 10%
- Biomedical Engineering
- Signal Processing
- Co-authors
- Dejan MarkovićSarah GibsonHariprasad ChandrakumarWenlong JiangZainul CharbiwalaMani SrivastavaJack W. JudyMatthew Spencer
- Topics
- Neuroscience and Neural Engineering (8 papers)Analog and Mixed-Signal Circuit Design (6 papers)Advanced Memory and Neural Computing (6 papers)
- Cited by
- Cellular and Molecular NeuroscienceCognitive NeuroscienceElectrical and Electronic Engineering
- Journals
- IEEE Journal of Solid-State CircuitsPubMedeScholarship (California Digital Library)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Vaibhav Karkare
12 papers receiving 381 citations
Peers
Comparison fields: 5 of 27
- Electrical and Electronic Engineering 292
- Cellular and Molecular Neuroscience 234
- Cognitive Neuroscience 196
- Biomedical Engineering 162
- Signal Processing 24
Countries citing papers authored by Vaibhav Karkare
This map shows the geographic impact of Vaibhav Karkare'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 Vaibhav Karkare with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vaibhav Karkare more than expected).
Fields of papers citing papers by Vaibhav Karkare
This network shows the impact of papers produced by Vaibhav Karkare. 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 Vaibhav Karkare. The network helps show where Vaibhav Karkare may publish in the future.
Co-authorship network of co-authors of Vaibhav Karkare
This figure shows the co-authorship network connecting the top 25 collaborators of Vaibhav Karkare. A scholar is included among the top collaborators of Vaibhav Karkare 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 Vaibhav Karkare. Vaibhav Karkare is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 15 | |
| 2 | 101 | |
| 3 | 10 | |
| 4 | Robust, Reconfigurable, and Power-Efficient Electrophysiological Recording Systems | 1 |
| 5 | 100 | |
| 6 | A 75µW, 16-channel neural spike-sorting processor with unsupervised clustering | 4 |
| 7 | 27 | |
| 8 | 78 | |
| 9 | 27 | |
| 10 | 6 | |
| 11 | 13 | |
| 12 | 8 |
About Vaibhav Karkare
Vaibhav Karkare is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Electrical and Electronic Engineering, having authored 12 papers that have together received 390 indexed citations. Recurring topics across this work include Neuroscience and Neural Engineering (8 papers), Analog and Mixed-Signal Circuit Design (6 papers) and Advanced Memory and Neural Computing (6 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (234 citations), Cognitive Neuroscience (196 citations) and Electrical and Electronic Engineering (292 citations). Vaibhav Karkare has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Dejan Marković, Sarah Gibson, Hariprasad Chandrakumar, Wenlong Jiang, Zainul Charbiwala, Mani Srivastava, Jack W. Judy, Matthew Spencer, Hei Kam and Chengcheng Wang. Their work appears in journals such as IEEE Journal of Solid-State Circuits, PubMed and eScholarship (California Digital Library).
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.