Ashwinkumar Ganesan

465 total citations
9 papers, 270 citations indexed

About

Ashwinkumar Ganesan is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Ashwinkumar Ganesan has authored 9 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Computer Networks and Communications and 2 papers in Information Systems. Recurrent topics in Ashwinkumar Ganesan's work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers) and Speech Recognition and Synthesis (2 papers). Ashwinkumar Ganesan is often cited by papers focused on Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers) and Speech Recognition and Synthesis (2 papers). Ashwinkumar Ganesan collaborates with scholars based in United States. Ashwinkumar Ganesan's co-authors include Tim Oates, James R. White, Julie C. Dunning Hotopp, Kelly Robinson, David R. Riley, Karsten B. Sieber, Tinoosh Mohsenin, Ali Jafari, Sandeep Nair Narayanan and Karuna Pande Joshi and has published in prestigious journals such as PLoS Computational Biology, IEEE Transactions on Circuits and Systems I Regular Papers and Maryland Shared Open Access Repository (USMAI Consortium).

In The Last Decade

Ashwinkumar Ganesan

8 papers receiving 262 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ashwinkumar Ganesan United States	5	93	67	35	30	30	9	270
G. Manjunath India	9	69 0.7×	156 2.3×	15 0.4×	25 0.8×	32 1.1×	31	338
Amit Jain India	9	59 0.6×	60 0.9×	24 0.7×	25 0.8×	27 0.9×	32	232
Mourad Elloumi Tunisia	10	172 1.8×	85 1.3×	29 0.8×	13 0.4×	56 1.9×	50	351
Manas Ranjan Prusty India	12	62 0.7×	145 2.2×	24 0.7×	32 1.1×	58 1.9×	32	458
Hongmei Wang China	12	64 0.7×	40 0.6×	41 1.2×	8 0.3×	56 1.9×	38	429
Liming Wang China	8	72 0.8×	63 0.9×	74 2.1×	18 0.6×	65 2.2×	16	460
G.R. Kulkarni India	10	125 1.3×	19 0.3×	15 0.4×	13 0.4×	13 0.4×	35	302
Yaw-Ling Lin Taiwan	11	205 2.2×	72 1.1×	8 0.2×	62 2.1×	28 0.9×	34	448
Shigeya Suzuki Japan	13	115 1.2×	59 0.9×	91 2.6×	92 3.1×	26 0.9×	52	444

Countries citing papers authored by Ashwinkumar Ganesan

Since Specialization

Citations

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

Fields of papers citing papers by Ashwinkumar Ganesan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashwinkumar Ganesan

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

All Works

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9 of 9 papers shown

1.

Xue, Rui, et al.. (2023). Improving Low Resource Speech Translation with Data Augmentation and Ensemble Strategies. 241–250.

2.

Ganesan, Ashwinkumar, et al.. (2022). L2-GEN: A Neural Phoneme Paraphrasing Approach to L2 Speech Synthesis for Mispronunciation Diagnosis. Interspeech 2022. 4317–4321. 4 indexed citations

3.

Jiang, Yu, et al.. (2022). Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies. 351–360. 1 indexed citations

4.

Ganesan, Ashwinkumar, Francis Ferraro, & Tim Oates. (2021). Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment. 4 indexed citations

5.

Waytowich, Nicholas R., et al.. (2020). Guiding Safe Reinforcement Learning Policies Using Structured Language Constraints. Maryland Shared Open Access Repository (USMAI Consortium). 153–161. 10 indexed citations

6.

Narayanan, Sandeep Nair, Ashwinkumar Ganesan, Karuna Pande Joshi, et al.. (2018). Early Detection of Cybersecurity Threats Using Collaborative Cognition. 354–363. 41 indexed citations

7.

Jafari, Ali, et al.. (2018). SensorNet: A Scalable and Low-Power Deep Convolutional Neural Network for Multimodal Data Classification. IEEE Transactions on Circuits and Systems I Regular Papers. 66(1). 274–287. 76 indexed citations

8.

Narayanan, Sandeep Nair, Ashwinkumar Ganesan, Karuna Pande Joshi, et al.. (2018). Early Detection of Cybersecurity Threats Using Collaborative Cognition. Maryland Shared Open Access Repository (USMAI Consortium). 12 indexed citations

9.

Riley, David R., Karsten B. Sieber, Kelly Robinson, et al.. (2013). Bacteria-Human Somatic Cell Lateral Gene Transfer Is Enriched in Cancer Samples. PLoS Computational Biology. 9(6). e1003107–e1003107. 122 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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