Pradeep Shenoy

3.3k total citations
39 papers, 2.3k citations indexed

About

Pradeep Shenoy is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Pradeep Shenoy has authored 39 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Cognitive Neuroscience, 10 papers in Artificial Intelligence and 7 papers in Cellular and Molecular Neuroscience. Recurrent topics in Pradeep Shenoy's work include EEG and Brain-Computer Interfaces (14 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neural Engineering (7 papers). Pradeep Shenoy is often cited by papers focused on EEG and Brain-Computer Interfaces (14 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neural Engineering (7 papers). Pradeep Shenoy collaborates with scholars based in United States, India and Germany. Pradeep Shenoy's co-authors include Rajesh P. N. Rao, Kai J. Miller, Angela J. Yu, Rawichote Chalodhorn, Desney Tan, Benjamin Blankertz, Matthias Krauledat, Klaus‐Robert Müller, Jeffrey G. Ojemann and Raghav Kaushik and has published in prestigious journals such as Journal of Neuroscience, NeuroImage and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Pradeep Shenoy

38 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pradeep Shenoy United States 21 1.5k 630 404 381 356 39 2.3k
A. P. Vinod Singapore 31 1.6k 1.1× 698 1.1× 352 0.9× 637 1.7× 1.5k 4.2× 250 3.6k
Ramaswamy Palaniappan United Kingdom 30 1.9k 1.3× 502 0.8× 351 0.9× 232 0.6× 645 1.8× 139 2.7k
Muhammad Tariq Sadiq China 28 1.2k 0.8× 270 0.4× 445 1.1× 462 1.2× 358 1.0× 67 2.3k
Xiang Zhang China 22 813 0.5× 202 0.3× 231 0.6× 132 0.3× 206 0.6× 106 1.6k
Chuang Lin China 21 736 0.5× 340 0.5× 180 0.4× 712 1.9× 106 0.3× 74 1.6k
Jinyi Long China 22 1.2k 0.8× 579 0.9× 270 0.7× 159 0.4× 163 0.5× 73 1.7k
Siuly Siuly Australia 38 3.1k 2.1× 536 0.9× 493 1.2× 320 0.8× 1.2k 3.3× 91 3.9k
Martin Bogdan Germany 27 1.9k 1.3× 785 1.2× 254 0.6× 351 0.9× 342 1.0× 103 2.6k
Bruno Arnaldi France 19 1.9k 1.3× 717 1.1× 250 0.6× 250 0.7× 499 1.4× 70 2.8k
Shyamanta M. Hazarika India 17 456 0.3× 104 0.2× 353 0.9× 188 0.5× 287 0.8× 82 1.2k

Countries citing papers authored by Pradeep Shenoy

Since Specialization
Citations

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

Fields of papers citing papers by Pradeep Shenoy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pradeep Shenoy

This figure shows the co-authorship network connecting the top 25 collaborators of Pradeep Shenoy. A scholar is included among the top collaborators of Pradeep Shenoy 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 Pradeep Shenoy. Pradeep Shenoy 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.
Jain, Nishant & Pradeep Shenoy. (2024). Instance-Conditional Timescales of Decay for Non-Stationary Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 12773–12781. 1 indexed citations
2.
Freyberg, Jan, et al.. (2023). Interactive Concept Bottleneck Models. Proceedings of the AAAI Conference on Artificial Intelligence. 37(5). 5948–5955. 14 indexed citations
3.
Iyer, Rishabh, et al.. (2022). GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 99–108. 71 indexed citations
4.
Shenoy, Pradeep, et al.. (2021). Tracking what matters: A decision-variable account of human behavior in bandit tasks. eScholarship (California Digital Library). 43(43). 2 indexed citations
5.
Shenoy, Pradeep, et al.. (2014). Ensemble PHOG and SIFT features extraction techniques to classify high resolution satellite images. ePrints@Bangalore University (Bangalore University). 6(5). 199–206. 3 indexed citations
6.
Harlé, Katia M., Pradeep Shenoy, Jennifer L. Stewart, et al.. (2014). Altered Neural Processing of the Need to Stop in Young Adults at Risk for Stimulant Dependence. Journal of Neuroscience. 34(13). 4567–4580. 32 indexed citations
7.
Harlé, Katia M., Pradeep Shenoy, & Martin P. Paulus. (2013). The influence of emotions on cognitive control: feelings and beliefs—where do they meet?. Frontiers in Human Neuroscience. 7. 508–508. 33 indexed citations
8.
Ide, Jaime S., Pradeep Shenoy, Angela J. Yu, & Chiang‐Shan R. Li. (2013). Bayesian Prediction and Evaluation in the Anterior Cingulate Cortex. Journal of Neuroscience. 33(5). 2039–2047. 149 indexed citations
9.
Shenoy, Pradeep & Angela J. Yu. (2012). Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making. TUbilio (Technical University of Darmstadt). 25. 2123–2131. 8 indexed citations
10.
Shenoy, Pradeep & Angela J. Yu. (2011). Rational Decision-Making in Inhibitory Control. Frontiers in Human Neuroscience. 5. 48–48. 61 indexed citations
11.
Shenoy, Pradeep, Angela J. Yu, & Rajesh P. N. Rao. (2010). A rational decision making framework for inhibitory control. TUbilio (Technical University of Darmstadt). 23. 2146–2154. 23 indexed citations
12.
Shenoy, Pradeep, et al.. (2008). Online Electromyographic Control of a Robotic Prosthesis. IEEE Transactions on Biomedical Engineering. 55(3). 1128–1135. 199 indexed citations
13.
Shenoy, Pradeep, Kai J. Miller, Jeffrey G. Ojemann, & Rajesh P. N. Rao. (2007). Generalized Features for Electrocorticographic BCIs. IEEE Transactions on Biomedical Engineering. 55(1). 273–280. 77 indexed citations
14.
Shenoy, Pradeep, et al.. (2007). An Image-based Brain-Computer Interface Using the P3 Response. 14. 318–321. 3 indexed citations
15.
Miller, Kai J., et al.. (2007). Real-time functional brain mapping using electrocorticography. NeuroImage. 37(2). 504–507. 115 indexed citations
16.
Shenoy, Pradeep, Matthias Krauledat, Benjamin Blankertz, Rajesh P. N. Rao, & Klaus‐Robert Müller. (2006). Towards adaptive classification for BCI. Journal of Neural Engineering. 3(1). R13–R23. 337 indexed citations
17.
Miller, Kai J., et al.. (2005). Real-time classification of electromyographic signals for robotic control. National Conference on Artificial Intelligence. 523–528. 92 indexed citations
18.
Shenoy, Pradeep & Rajesh P. N. Rao. (2004). Dynamic Bayesian Networks for Brain-Computer Interfaces. Neural Information Processing Systems. 17. 1265–1272. 20 indexed citations
19.
Kaushik, Raghav, Pradeep Shenoy, Philip Bohannon, & Ehud Gudes. (2002). Exploiting Local Similarity to Efficiently Index Paths in Graph-Structured Data. 9 indexed citations
20.
Shenoy, Pradeep, Jayant R. Haritsa, S. Sudarshan, et al.. (2000). VIPER: A Vertical Approach to Mining Association Rules. 2 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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