Devika Subramanian

3.2k total citations
82 papers, 2.0k citations indexed

About

Devika Subramanian is a scholar working on Artificial Intelligence, Computer Networks and Communications and Molecular Biology. According to data from OpenAlex, Devika Subramanian has authored 82 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 19 papers in Computer Networks and Communications and 13 papers in Molecular Biology. Recurrent topics in Devika Subramanian's work include AI-based Problem Solving and Planning (10 papers), Logic, programming, and type systems (9 papers) and Parallel Computing and Optimization Techniques (9 papers). Devika Subramanian is often cited by papers focused on AI-based Problem Solving and Planning (10 papers), Logic, programming, and type systems (9 papers) and Parallel Computing and Optimization Techniques (9 papers). Devika Subramanian collaborates with scholars based in United States, Chile and Singapore. Devika Subramanian's 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 and has published in prestigious journals such as Gastroenterology, The Journal of Clinical Endocrinology & Metabolism and Clinical Infectious Diseases.

In The Last Decade

Devika Subramanian

80 papers receiving 1.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Devika Subramanian United States 23 621 547 476 311 202 82 2.0k
Rajarshi Das United States 23 1.3k 2.1× 240 0.4× 1.1k 2.2× 981 3.2× 65 0.3× 67 2.9k
Ajit Singh India 17 523 0.8× 105 0.2× 332 0.7× 212 0.7× 94 0.5× 126 1.7k
Zhishan Guo United States 24 375 0.6× 681 1.2× 539 1.1× 107 0.3× 67 0.3× 121 1.7k
Marcin Paprzycki Poland 17 550 0.9× 139 0.3× 705 1.5× 384 1.2× 88 0.4× 215 1.8k
Heinz Mühlenbein Germany 25 2.6k 4.2× 91 0.2× 332 0.7× 178 0.6× 115 0.6× 62 3.9k
Lyle A. McGeoch United States 15 693 1.1× 225 0.4× 1.7k 3.5× 148 0.5× 90 0.4× 26 3.4k
Spiros Papadimitriou United States 23 1.5k 2.3× 85 0.2× 773 1.6× 547 1.8× 100 0.5× 53 2.7k
Gabriel Wainer Canada 22 124 0.2× 194 0.4× 934 2.0× 198 0.6× 40 0.2× 336 2.7k
Huy T. Vo United States 24 386 0.6× 485 0.9× 828 1.7× 529 1.7× 89 0.4× 72 2.8k
Vipin Kumar United States 20 889 1.4× 304 0.6× 873 1.8× 326 1.0× 21 0.1× 67 2.0k

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-authorship network of co-authors of Devika Subramanian

This figure shows the co-authorship network connecting the top 25 collaborators of Devika Subramanian. A scholar is included among the top collaborators of Devika Subramanian 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 Devika Subramanian. Devika Subramanian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pozdeyev, Nikita, Manjiri Dighe, Martin Barrio, et al.. (2023). OR32-02 Thyroid Cancer Polygenic Risk Score Improves Risk Stratification Of Thyroid Nodules When Added To Ultrasound Imaging. Journal of the Endocrine Society. 7(Supplement_1). 1 indexed citations
2.
Subramanian, Devika, et al.. (2021). Domain-driven models yield better predictions at lower cost than reservoir computers in Lorenz systems. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 379(2194). 20200246–20200246. 18 indexed citations
3.
Shams, Shayan, Yejin Kim, Ananth Annapragada, et al.. (2021). Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates. Journal of Biomedical Informatics. 119. 103818–103818. 4 indexed citations
4.
Chattopadhyay, Ashesh, Pedram Hassanzadeh, & Devika Subramanian. (2020). Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM. 1 indexed citations
5.
Chattopadhyay, Ashesh, Pedram Hassanzadeh, Krishna V. Palem, & Devika Subramanian. (2019). Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM. arXiv (Cornell University). 20 indexed citations
6.
7.
Herskovic, Jorge R, et al.. (2012). Graph-based signal integration for high-throughput phenotyping. BMC Bioinformatics. 13(S13). S2–S2. 4 indexed citations
8.
Herskovic, Jorge R, Trevor Cohen, Devika Subramanian, et al.. (2011). MEDRank: Using graph-based concept ranking to index biomedical texts. International Journal of Medical Informatics. 80(6). 431–441. 14 indexed citations
9.
Stein, Robert M., Leonardo Dueñas‐Osorio, & Devika Subramanian. (2010). Who Evacuates When Hurricanes Approach? The Role of Risk, Information, and Location*. Social Science Quarterly. 91(3). 816–834. 101 indexed citations
10.
Broom, Bradley M., et al.. (2009). Learning robust cell signalling models from high throughput proteomic data. International Journal of Bioinformatics Research and Applications. 5(3). 241–241. 3 indexed citations
11.
Buckley, M. Ronald, et al.. (2008). Socially relevant computing. ACM SIGCSE Bulletin. 40(1). 347–351. 14 indexed citations
12.
Buchanan, Bruce G., et al.. (2000). Statistical methods for the objective design of screening procedures for macromolecular crystallization. Acta Crystallographica Section D Biological Crystallography. 56(7). 817–827. 36 indexed citations
13.
Subramanian, Devika, et al.. (1997). Ants and reinforcement learning: a case study in routing in dynamic networks. International Joint Conference on Artificial Intelligence. 832–838. 150 indexed citations
14.
Subramanian, Devika, Russell Greiner, & Judea Pearl. (1997). The relevance of relevance. Artificial Intelligence. 97(1-2). 1–5. 23 indexed citations
15.
Gopalakrishnan, Vanathi, et al.. (1994). The crystallographer's assistant. National Conference on Artificial Intelligence. 1451–1451. 1 indexed citations
16.
Rus, Daniela & Devika Subramanian. (1993). Multi-media RISSC Informatics: Retrieval of Information with Simple Structural Components (Part I: The Architecture).. 283–294. 3 indexed citations
17.
Gordon, Diana F. & Devika Subramanian. (1993). A Multistrategy Learning Scheme for Agent Knowledge Acquisition.. Informatica (slovenia). 17. 29 indexed citations
18.
Gunter, Carl A., et al.. (1991). The common order-theoretic structure of version spaces and ATMS's. ScholarlyCommons (University of Pennsylvania). 500–505. 11 indexed citations
19.
Subramanian, Devika & Ronen Feldman. (1990). The utility of ebl in recursive domain theories. National Conference on Artificial Intelligence. 942–949. 16 indexed citations
20.
Subramanian, Devika & Joan Feigenbaum. (1986). Factorization in experiment generation. National Conference on Artificial Intelligence. 518–522. 27 indexed citations

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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