Devika Subramanian

3.2k citations
82 papers · 2.0k · h-index 23

Impact in

    • Parallel Computing and Optimization Techniques
    • Embedded Systems Design Techniques
  • Software top 5%
    • Software Testing and Debugging Techniques

Papers in

Devika Subramanian

80 papers receiving 1.8k citations

Peers

Devika Subramanian
Comparison fields: 5 of 140
  • Hardware and Architecture 547
  • Software 120
  • Computer Networks and Communications 476
  • Artificial Intelligence 621
  • Information Systems 311
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Citations per field
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Citations per year

Countries citing papers authored by Devika Subramanian

Since Specialization
Citations

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

Fields of papers citing papers by Devika Subramanian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 82 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999209
2 2009204
3
Ants and reinforcement learning: a case study in routing in dynamic networks
1997150
4 2002120
5 2004117
6 2010101
7 200271
8 200568
9 200850
10 200249
11 201148
12 200036
13 201336
14 199535
15 199935
16 199534
17 199231
18 200430
19
A Multistrategy Learning Scheme for Agent Knowledge Acquisition.
199329
20
Factorization in experiment generation
198627

About Devika Subramanian

Devika Subramanian is a scholar working on Artificial Intelligence, Computer Networks and Communications, Molecular Biology, Computational Theory and Mathematics and Sociology and Political Science, having authored 82 papers that have together received 2.0k indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (10 papers), Parallel Computing and Optimization Techniques (9 papers), Logic, programming, and type systems (9 papers), Biomedical Text Mining and Ontologies (8 papers), Pharmacovigilance and Adverse Drug Reactions (7 papers), Computational Drug Discovery Methods (7 papers), Disaster Management and Resilience (5 papers) and Bioinformatics and Genomic Networks (4 papers). The work is most often cited by research in Hardware and Architecture (547 citations), Software (120 citations), Computer Networks and Communications (476 citations), Artificial Intelligence (621 citations) and Information Systems (311 citations). Devika Subramanian has collaborated with scholars based in United States, Chile and Singapore. Frequent co-authors include Keith D. Cooper, Robert M. Stein, Leonardo Dueñas‐Osorio, Philip J. Schielke, Linda Torczon, Peter Druschel, James D. Winkler, Timothy J. Harvey, Alexander Grosul and Todd Waterman. Their work appears in journals such as BMC Bioinformatics, ACM SIGPLAN Notices, Artificial Intelligence, Journal of Biomedical Informatics and Natural Hazards Review.

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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