Madhav Sukumaran

740 citations
12 papers · 371 indexed · h-index 9

Madhav Sukumaran

12 papers receiving 364 citations

Peers

Madhav Sukumaran
Comparison fields: 5 of 62
  • Virology 58
  • Cellular and Molecular Neuroscience 202
  • Human-Computer Interaction 23
  • Molecular Biology 207
  • Cognitive Neuroscience 39
Replace Jason T. Newman with:
Jason T. Newman United States
Callista B. Harper Australia
Ernesto Ciabatti United Kingdom
Beena M. Kadakkuzha United States
Gertrud Goping United States
Rainer Pielot Germany
Linda A. Landon United States
Florence Chiodini Switzerland
Fa‐Yun Che United States
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Madhav Sukumaran relative to Jason T. Newman United States Jason T. Newman's profile →
Citations per field
00.5×4.8×
Jason T. Newman · 1×
Citations per year

Countries citing papers authored by Madhav Sukumaran

Since Specialization
Citations

This map shows the geographic impact of Madhav Sukumaran'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 Madhav Sukumaran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Madhav Sukumaran more than expected).

Fields of papers citing papers by Madhav Sukumaran

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Madhav Sukumaran. 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 Madhav Sukumaran. The network helps show where Madhav Sukumaran may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Madhav Sukumaran, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Madhav Sukumaran Line = papers co-authored together Madhav Sukumaran links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 20256
2 20245
3 201614
4 201533
5 201223
6 201235
7 201153
8 201116
9 201192
10 200966
11 200624
12 20064

About Madhav Sukumaran

Madhav Sukumaran is a scholar working on Aging, Human-Computer Interaction, Cellular and Molecular Neuroscience, Virology and Rheumatology, having authored 12 papers that have together received 371 indexed citations. Recurring topics across this work include Neuroscience and Neuropharmacology Research (5 papers), Receptor Mechanisms and Signaling (3 papers), Photoreceptor and optogenetics research (2 papers), Ion channel regulation and function (2 papers), Gaze Tracking and Assistive Technology (2 papers), Teleoperation and Haptic Systems (2 papers), Moyamoya disease diagnosis and treatment (1 paper) and Head and Neck Surgical Oncology (1 paper). The work is most often cited by research in Virology (58 citations), Cellular and Molecular Neuroscience (202 citations), Human-Computer Interaction (23 citations), Molecular Biology (207 citations) and Cognitive Neuroscience (39 citations). Madhav Sukumaran has collaborated with scholars based in United States, United Kingdom and France. Frequent co-authors include Ingo H. Greger, Maxim Rossmann, Andrew C. Penn, Anindita Dutta, İvet Bahar, Indira H. Shrivastava, M. Madan Babu, Dmitry B. Veprintsev, Xiaohong Wang and Miroslaw K. Górny. Their work appears in journals such as The EMBO Journal, Journal of neurosurgery, Structure, World Neurosurgery and Advances in experimental medicine and biology.

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.

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