Naren Ramakrishnan

10.9k total citations
355 papers, 5.9k citations indexed

About

Naren Ramakrishnan is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Naren Ramakrishnan has authored 355 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 153 papers in Artificial Intelligence, 60 papers in Information Systems and 52 papers in Molecular Biology. Recurrent topics in Naren Ramakrishnan's work include Data-Driven Disease Surveillance (47 papers), Complex Network Analysis Techniques (40 papers) and Data Visualization and Analytics (34 papers). Naren Ramakrishnan is often cited by papers focused on Data-Driven Disease Surveillance (47 papers), Complex Network Analysis Techniques (40 papers) and Data Visualization and Analytics (34 papers). Naren Ramakrishnan collaborates with scholars based in United States, India and Canada. Naren Ramakrishnan's co-authors include Chang‐Tien Lu, Liang Zhao, Ananth Grama, Feng Chen, Benjamin J. Keller, Manish Marwah, Madhav Marathe, P. J. Butler, Fang Jin and Chris North and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Naren Ramakrishnan

342 papers receiving 5.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Naren Ramakrishnan United States 41 2.1k 1.1k 822 734 704 355 5.9k
Wagner Meira Brazil 40 2.1k 1.0× 1.9k 1.8× 580 0.7× 1.8k 2.5× 547 0.8× 317 6.5k
Dino Pedreschi Italy 43 4.1k 1.9× 1.1k 1.0× 927 1.1× 820 1.1× 799 1.1× 204 9.1k
Prasenjit Mitra United States 39 3.4k 1.6× 2.0k 1.9× 786 1.0× 722 1.0× 778 1.1× 234 5.8k
Jing Gao United States 37 4.0k 1.9× 1.2k 1.1× 378 0.5× 648 0.9× 1.1k 1.5× 164 6.4k
Pádraig Cunningham Ireland 37 3.1k 1.5× 1.1k 1.1× 633 0.8× 587 0.8× 950 1.3× 205 5.9k
Jianxin Li China 44 2.6k 1.2× 1.3k 1.3× 940 1.1× 1.4k 1.9× 822 1.2× 398 7.1k
Steven Skiena United States 37 2.1k 1.0× 407 0.4× 468 0.6× 447 0.6× 647 0.9× 197 6.5k
Tony Jebara United States 33 2.3k 1.1× 1.0k 0.9× 932 1.1× 332 0.5× 2.1k 3.0× 103 6.4k
Reda Alhajj Canada 36 2.1k 1.0× 1.2k 1.1× 380 0.5× 995 1.4× 704 1.0× 404 5.1k
Josep M. Pujol Spain 15 2.4k 1.1× 996 0.9× 502 0.6× 723 1.0× 1.0k 1.5× 27 5.1k

Countries citing papers authored by Naren Ramakrishnan

Since Specialization
Citations

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

Fields of papers citing papers by Naren Ramakrishnan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Naren Ramakrishnan

This figure shows the co-authorship network connecting the top 25 collaborators of Naren Ramakrishnan. A scholar is included among the top collaborators of Naren Ramakrishnan 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 Naren Ramakrishnan. Naren Ramakrishnan 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.
Thomas, Christo Kurisummoottil, et al.. (2024). Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems. IEEE Network. 38(5). 10–20. 20 indexed citations
2.
Rodríguez, Alexander, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, & B. Aditya Prakash. (2023). EINNs: Epidemiologically-Informed Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 37(12). 14453–14460. 11 indexed citations
3.
Muralidhar, Nikhil, et al.. (2022). Efficient Generative Wireless Anomaly Detection for Next Generation Networks. 594–599. 2 indexed citations
4.
Muralidhar, Nikhil, et al.. (2021). PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields. 1246–1251. 2 indexed citations
5.
Chen, Feng, et al.. (2018). A Nonparametric Approach to Uncovering Connected Anomalies by Tree Shaped Priors. IEEE Transactions on Knowledge and Data Engineering. 31(10). 1849–1862. 6 indexed citations
6.
Boedihardjo, Arnold P., et al.. (2016). The big data of violent events: algorithms for association analysis using spatio-temporal storytelling. GeoInformatica. 20(4). 879–921. 6 indexed citations
7.
Shah, Sumit, Arnold P. Boedihardjo, Feng Chen, et al.. (2015). A framework for intelligence analysis using spatio-temporal storytelling. GeoInformatica. 20(2). 285–326. 7 indexed citations
8.
Zhao, Liang, Jiangzhuo Chen, Feng Chen, et al.. (2015). SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning. PubMed. 2015. 639–648. 45 indexed citations
9.
Katz, Graham, Kristen Summers, Chris Ackermann, et al.. (2014). Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System. Big Data. 2(4). 185–195. 19 indexed citations
10.
Chakraborty, Prithwish, Pejman Khadivi, Bryan Lewis, et al.. (2014). Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions. 262–270. 51 indexed citations
11.
Ramakrishnan, Naren, et al.. (2014). Sanstext: classifying temporal topic dynamics of twitter cascades without tweet text. 649–656. 2 indexed citations
12.
Marathe, Madhav & Naren Ramakrishnan. (2013). Recent Advances in Computational Epidemiology. IEEE Intelligent Systems. 28(4). 96–101. 24 indexed citations
13.
Hossain, M. Shahriar, Christopher Andrews, Naren Ramakrishnan, & Chris North. (2011). Helping intelligence analysts make connections. National Conference on Artificial Intelligence. 22–31. 18 indexed citations
14.
Ramakrishnan, Naren, et al.. (2010). Finding the Storyteller: Automatic Spoiler Tagging using Linguistic Cues. International Conference on Computational Linguistics. 412–420. 11 indexed citations
15.
Perugini, Saverio, et al.. (2009). Exploring Out-of-turn Interactions with Websites. Texas Digital Library (University of Texas). 10(4). 1. 8 indexed citations
16.
Kleinberg, Samantha, Marco Antoniotti, Naren Ramakrishnan, & Bharat Mishra. (2007). Modal Logic, Temporal Models and Neural Circuits: What Connects Them. BOA (University of Milano-Bicocca). 1 indexed citations
17.
Bailey‐Kellogg, Chris & Naren Ramakrishnan. (2004). Spatial aggregation for qualitative assessment of scientific computations. National Conference on Artificial Intelligence. 585–591. 2 indexed citations
18.
Capra, R., et al.. (2003). The Staging Transformation Approach to Mixing Initiative. International Joint Conference on Artificial Intelligence. 5 indexed citations
19.
20.
Ramakrishnan, Naren & Chris Bailey‐Kellogg. (2003). Gaussian process models of spatial aggregation algorithms. International Joint Conference on Artificial Intelligence. 1045–1051. 1 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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