Suhas Kumar
- Polymers and Plastics top 2%
- Transition Metal Oxide Nanomaterials 15
-
- Neuroscience and Neural Engineering 9
- Photoreceptor and optogenetics research 5
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- Advanced Memory and Neural Computing 44
- Ferroelectric and Negative Capacitance Devices 19
- Gas Sensing Nanomaterials and Sensors 5
- Cognitive Neuroscience top 2%
- Neural dynamics and brain function 9
- Artificial Intelligence top 2%
- Neural Networks and Reservoir Computing 10
- Co-authors
- R. Stanley WilliamsJohn Paul StrachanZiwen WangYoshio NishiWei LüXinxin WangYuchao YangA. L. D. Kilcoyne
- Cited by
- Polymers and PlasticsCellular and Molecular NeuroscienceElectrical and Electronic Engineering
- Partner nations
- United StatesSouth KoreaSpain
In The Last Decade
Suhas Kumar
55 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Polymers and Plastics 681
- Cellular and Molecular Neuroscience 887
- Electrical and Electronic Engineering 2.7k
- Cognitive Neuroscience 708
- Artificial Intelligence 593
Countries citing papers authored by Suhas Kumar
This map shows the geographic impact of Suhas Kumar'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 Suhas Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suhas Kumar more than expected).
Fields of papers citing papers by Suhas Kumar
This network shows the impact of papers produced by Suhas Kumar. 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 Suhas Kumar. The network helps show where Suhas Kumar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Suhas Kumar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 3 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 10 | |
| 6 | 2024 | 11 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 20 | |
| 9 | 2024 | 29 | |
| 10 | 2024 | 27 | |
| 11 | 2024 | 0 | |
| 12 | 2024 | 0 | |
| 13 | 2024 | 39 | |
| 14 | 2023 | 3 | |
| 15 | 2023 | 28 | |
| 16 | 2022 | 55 | |
| 17 | 2021 | 7 | |
| 18 | Third-order nanocircuit elements for neuromorphic engineeringbreakdown → | 2020 | 268 |
| 19 | Charge sensing by altering the phase transition in VO2 | 2014 | 0 |
| 20 | 2013 | 187 |
About Suhas Kumar
Suhas Kumar is a scholar working on Structural Biology, Polymers and Plastics and Electrical and Electronic Engineering, having authored 62 papers that have together received 3.0k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (44 papers), Ferroelectric and Negative Capacitance Devices (19 papers), Transition Metal Oxide Nanomaterials (15 papers), Neural Networks and Reservoir Computing (10 papers), Neural dynamics and brain function (9 papers), Neuroscience and Neural Engineering (9 papers), Photoreceptor and optogenetics research (5 papers) and Gas Sensing Nanomaterials and Sensors (5 papers). The work is most often cited by research in Polymers and Plastics (681 citations), Cellular and Molecular Neuroscience (887 citations) and Electrical and Electronic Engineering (2.7k citations). Suhas Kumar has collaborated with scholars based in United States, South Korea and Spain. Frequent co-authors include R. Stanley Williams, John Paul Strachan, Ziwen Wang, Yoshio Nishi, Wei Lü, Xinxin Wang, Yuchao Yang, A. L. D. Kilcoyne, Matthew D. Pickett and D. J. Vine. Their work appears in journals such as Advanced Materials, Applied Physics Letters, ACS Nano, Nature Communications and Nature.
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.