Ankur Teredesai

2.7k total citations
83 papers, 1.6k citations indexed

About

Ankur Teredesai is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ankur Teredesai has authored 83 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 20 papers in Information Systems and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ankur Teredesai's work include Machine Learning in Healthcare (17 papers), Data Management and Algorithms (9 papers) and Artificial Intelligence in Healthcare (6 papers). Ankur Teredesai is often cited by papers focused on Machine Learning in Healthcare (17 papers), Data Management and Algorithms (9 papers) and Artificial Intelligence in Healthcare (6 papers). Ankur Teredesai collaborates with scholars based in United States, Belgium and France. Ankur Teredesai's co-authors include Muhammad Aurangzeb Ahmad, Carly Eckert, Martine De Cock, Patricia Victor, Chris Cornelis, Howard Routman, Christopher Roche, Pierre-Henri Flurin, Thomas W. Wright and Ryan W. Simovitch and has published in prestigious journals such as Clinical Orthopaedics and Related Research, Pattern Recognition and Journal of Shoulder and Elbow Surgery.

In The Last Decade

Ankur Teredesai

80 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ankur Teredesai United States	20	638	348	270	218	195	83	1.6k
Feipei Lai Taiwan	30	774 1.2×	542 1.6×	231 0.9×	237 1.1×	255 1.3×	293	3.5k
Carsten Eickhoff United States	20	710 1.1×	361 1.0×	96 0.4×	174 0.8×	66 0.3×	117	1.8k
Mario Stefanelli Italy	31	1.0k 1.6×	251 0.7×	150 0.6×	99 0.5×	711 3.6×	122	3.2k
Manuel Filipe Santos Portugal	16	384 0.6×	229 0.7×	152 0.6×	86 0.4×	347 1.8×	222	1.4k
Shyam Visweswaran United States	23	721 1.1×	102 0.3×	109 0.4×	181 0.8×	243 1.2×	129	1.9k
Leila Shahmoradi Iran	23	511 0.8×	206 0.6×	73 0.3×	147 0.7×	463 2.4×	122	2.1k
Xudong Lü China	25	752 1.2×	359 1.0×	41 0.2×	98 0.4×	317 1.6×	155	2.1k
Gang Luo United States	25	818 1.3×	275 0.8×	47 0.2×	113 0.5×	190 1.0×	139	2.0k
Mark Hoogendoorn Netherlands	19	828 1.3×	80 0.2×	81 0.3×	308 1.4×	101 0.5×	138	1.9k
Xujuan Zhou Australia	24	920 1.4×	366 1.1×	32 0.1×	139 0.6×	231 1.2×	97	2.1k

Countries citing papers authored by Ankur Teredesai

Since Specialization

Citations

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

Fields of papers citing papers by Ankur Teredesai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ankur Teredesai

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

All Works

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20 of 20 papers shown

Teredesai, Ankur, et al.. (2024). NudgeRank: Digital Algorithmic Nudging for Personalized Health. arXiv (Cornell University). 4873–4884. 2 indexed citations

Martínez, Óscar Sanjuán, et al.. (2023). Blockchain Based Cloud Management Architecture for Maximum Availability.. International Journal of Interactive Multimedia and Artificial Intelligence. 8(1). 88–94. 10 indexed citations

Teredesai, Ankur, et al.. (2022). Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care. 2022 IEEE International Conference on Big Data (Big Data). 396. 2082–2092. 1 indexed citations

Kumar, Vikas, Bradley S. Schoch, Ankur Teredesai, et al.. (2021). Using machine learning to predict internal rotation after anatomic and reverse total shoulder arthroplasty. Journal of Shoulder and Elbow Surgery. 31(5). e234–e245. 31 indexed citations

Roche, Christopher, Steven Overman, Ryan W. Simovitch, et al.. (2021). Validation of a machine learning–derived clinical metric to quantify outcomes after total shoulder arthroplasty. Journal of Shoulder and Elbow Surgery. 30(10). 2211–2224. 77 indexed citations

Ahmad, Muhammad Aurangzeb, et al.. (2021). Machine Learning Approaches for Patient State Prediction in Pediatric ICUs. 422–426.

Kumar, Vikas, Christopher Roche, Steven Overman, et al.. (2020). Using machine learning to predict clinical outcomes after shoulder arthroplasty with a minimal feature set. Journal of Shoulder and Elbow Surgery. 30(5). e225–e236. 60 indexed citations

Kumar, Vikas, Christopher Roche, Steven Overman, et al.. (2020). What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?. Clinical Orthopaedics and Related Research. 478(10). 2351–2363. 65 indexed citations

Ahmad, Muhammad Aurangzeb, Carly Eckert, & Ankur Teredesai. (2019). The Challenge of Imputation in Explainable Artificial Intelligence Models. International Joint Conference on Artificial Intelligence. 1 indexed citations

10.

Ahmad, Muhammad Aurangzeb, Ankur Teredesai, & Carly Eckert. (2018). Interpretable Machine Learning in Healthcare. 447–447. 135 indexed citations

11.

Sushmita, Shanu, et al.. (2016). Predicting 30-Day Risk and Cost of "All-Cause" Hospital Readmissions. National Conference on Artificial Intelligence. 453–461. 14 indexed citations

12.

Kolobov, Andrey, et al.. (2015). Selecting Robust Strategies in RTS Games via Concurrent Plan Augmentation. Adaptive Agents and Multi-Agents Systems. 155–162. 1 indexed citations

13.

Farnadi, Golnoosh, et al.. (2014). Age and gender identification in social media. Ghent University Academic Bibliography (Ghent University). 1129–1136. 38 indexed citations

14.

Teredesai, Ankur, et al.. (2010). Born to trade: A genetically evolved keyword bidder for sponsored search. Ghent University Academic Bibliography (Ghent University). 78. 1–8. 4 indexed citations

15.

Victor, Patricia, Chris Cornelis, Martine De Cock, & Ankur Teredesai. (2009). Trust- and Distrust-Based Recommendations for Controversial Reviews. Ghent University Academic Bibliography (Ghent University). 18 indexed citations

16.

Ahmad, Muhammad Aurangzeb & Ankur Teredesai. (2006). Modeling spread of ideas in online social networks. 185–190. 4 indexed citations

17.

Ahmad, Muhammad Aurangzeb & Ankur Teredesai. (2006). Modeling Proliferation of Ideas in Online Social Networks.. 185–190. 1 indexed citations

18.

Chaoji, Vineet, et al.. (2004). VENUS: A System for Novelty Detection in Video Streams with Learning.. The Florida AI Research Society. 232–238. 6 indexed citations

19.

Teredesai, Ankur & Venu Govindaraju. (2004). Issues in evolving GP based classifiers for a pattern recognition task. 509–515. 17 indexed citations

20.

Teredesai, Ankur, E. Ratzlaff, J. Subrahmonia, & Venu Govindaraju. (2002). On-Line Digit Recognition Using Off-Line Features.. 6 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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