Vikram Suresh

1.1k citations
71 papers · 859 · h-index 15

Impact in

Papers in

Vikram Suresh

67 papers receiving 847 citations

Peers

Vikram Suresh
Comparison fields: 5 of 70
  • Hardware and Architecture 493
  • Computer Vision and Pattern Recognition 256
  • Artificial Intelligence 296
  • Electrical and Electronic Engineering 410
  • Computational Theory and Mathematics 110
Replace Steven K. Hsu with:
Steven K. Hsu United States
Farhana Sheikh United States
Sudhir Satpathy United States
Qiang Zhou China
Alexander Fish Israel
Melissa C. Smith United States
Jintao Zhang China
Kanad Basu United States
Shan Gai China
Md Tauhidur Rahman United States
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Citations per field
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Citations per year

Countries citing papers authored by Vikram Suresh

Since Specialization
Citations

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

Fields of papers citing papers by Vikram Suresh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Vikram Suresh, 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 Vikram Suresh Line = papers co-authored together Vikram Suresh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 71 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015108
2 201799
3 201680
4 201979
5 201544
6 201833
7 202129
8 201029
9 202221
10 202019
11 202118
12 201817
13 202017
14 201317
15 202117
16 201814
17 201913
18 202011
19 202210
20 201110

About Vikram Suresh

Vikram Suresh is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture, Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 71 papers that have together received 859 indexed citations. Recurring topics across this work include Physical Unclonable Functions (PUFs) and Hardware Security (28 papers), Cryptographic Implementations and Security (15 papers), Advanced Memory and Neural Computing (13 papers), Low-power high-performance VLSI design (11 papers), Integrated Circuits and Semiconductor Failure Analysis (11 papers), Quantum-Dot Cellular Automata (9 papers), Semiconductor materials and devices (8 papers) and Chaos-based Image/Signal Encryption (8 papers). The work is most often cited by research in Hardware and Architecture (493 citations), Computer Vision and Pattern Recognition (256 citations), Artificial Intelligence (296 citations), Electrical and Electronic Engineering (410 citations) and Computational Theory and Mathematics (110 citations). Vikram Suresh has collaborated with scholars based in United States, India and Italy. Frequent co-authors include Sanu Mathew, Mark Anders, Himanshu Kaul, Sudhir Satpathy, Ram Krishnamurthy, Wayne Burleson, Amit Agarwal, Steven Hsu, Vivek De and Gregory Chen. Their work appears in journals such as IEEE Journal of Solid-State Circuits, Journal of Aerosol Science, Lab on a Chip, ACM Journal on Emerging Technologies in Computing Systems and Indian Journal of Science and Technology.

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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