Rishabh Singh

860 total citations
24 papers, 579 citations indexed

About

Rishabh Singh is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Rishabh Singh has authored 24 papers receiving a total of 579 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 3 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Rishabh Singh's work include Neural Networks and Applications (4 papers), Computational Drug Discovery Methods (3 papers) and Biomedical Ethics and Regulation (2 papers). Rishabh Singh is often cited by papers focused on Neural Networks and Applications (4 papers), Computational Drug Discovery Methods (3 papers) and Biomedical Ethics and Regulation (2 papers). Rishabh Singh collaborates with scholars based in United States, India and Germany. Rishabh Singh's co-authors include Peter Laux, Daniel Rosenkranz, Andreas Luch, Anurag Kanase, Ajay Vikram Singh, Mohammad Hasan Dad Ansari, Fabian L. Kriegel, Romi Singh Maharjan, Katherina Siewert and Shubham Singh and has published in prestigious journals such as Journal of Clinical Oncology, Blood and ACS Applied Materials & Interfaces.

In The Last Decade

Rishabh Singh

21 papers receiving 559 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rishabh Singh United States 7 182 150 104 92 66 24 579
Romi Singh Maharjan Germany 9 162 0.9× 135 0.9× 112 1.1× 84 0.9× 55 0.8× 10 599
Anurag Kanase India 9 210 1.2× 170 1.1× 134 1.3× 118 1.3× 84 1.3× 10 668
Daniel Rosenkranz Germany 8 203 1.1× 181 1.2× 114 1.1× 98 1.1× 70 1.1× 11 591
Mohammad Hasan Dad Ansari Italy 12 467 2.6× 178 1.2× 122 1.2× 78 0.8× 94 1.4× 15 857
Faheem Ahmed South Korea 19 260 1.4× 57 0.4× 228 2.2× 97 1.1× 22 0.3× 48 923
Yingjie Ma China 15 150 0.8× 104 0.7× 293 2.8× 40 0.4× 120 1.8× 46 902
Graeme Clemens United Kingdom 15 354 1.9× 168 1.1× 219 2.1× 68 0.7× 15 0.2× 28 903
Zeqing Bao Canada 11 209 1.1× 198 1.3× 135 1.3× 140 1.5× 69 1.0× 22 608
Yun South Korea 9 135 0.7× 119 0.8× 90 0.9× 32 0.3× 37 0.6× 92 561
Nikhil Agrawal South Africa 18 97 0.5× 57 0.4× 324 3.1× 28 0.3× 114 1.7× 55 915

Countries citing papers authored by Rishabh Singh

Since Specialization
Citations

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

Fields of papers citing papers by Rishabh Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rishabh Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Rishabh Singh. A scholar is included among the top collaborators of Rishabh Singh 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 Rishabh Singh. Rishabh Singh 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.
Singh, Rishabh, et al.. (2025). Patient-centered perspectives: Examining quality-of-life integration in recent phase III lung cancer trials (2019–2023). Journal of Cancer Policy. 44. 100566–100566. 1 indexed citations
2.
Singh, Rishabh, Caroline Cromwell, Manila Gaddh, et al.. (2024). Comparative Analysis of Perioperative Anticoagulation Management Guidelines across U.S. Institutions. Blood. 144(Supplement 1). 5014–5014.
3.
Kalantri, Shriprakash, et al.. (2024). P4.17F.06 Review of State Legislation Mandating Biomarker Coverage in Cancer Care. Journal of Thoracic Oncology. 19(10). S426–S426. 1 indexed citations
4.
Singh, Rishabh, et al.. (2024). The Efficacy and Safety of CD-19 Directed CAR-NK Therapy in Adults with B-Cell Malignancies: A Meta-Analysis. Blood. 144(Supplement 1). 7180–7180. 2 indexed citations
5.
Singh, Rishabh, Michal Kubiak, Mohammad Mian, et al.. (2023). Single center experience of using Anakinra prophylactically at Day 0 to reduce CD19 CART toxicities.. Journal of Clinical Oncology. 41(16_suppl). e19503–e19503.
6.
Singh, Rishabh, et al.. (2023). Automation of Brain Tumor Identification using EfficientNet on Magnetic Resonance Images. Procedia Computer Science. 218. 1551–1560. 30 indexed citations
7.
Singh, Rishabh & Ashish Singhal. (2023). Artificial Intelligence based Technique for Solar Irradiance Prediction Model with Improved Performance. 1–6. 2 indexed citations
8.
Singh, Rishabh, et al.. (2022). Prediction of Stress Level on Indian Working Professionals Using Machine Learning. International Journal of Human Capital and Information Technology Professionals. 13(1). 1–26. 3 indexed citations
9.
Singh, Rishabh & José C. Prı́ncipe. (2021). Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models. Neural Computation. 33(5). 1164–1198.
10.
Singh, Rishabh & José C. Prı́ncipe. (2020). Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework. Uncertainty in Artificial Intelligence. 1368–1377. 2 indexed citations
11.
Singh, Rishabh, et al.. (2020). Resizing Tiny Imagenet: An Iterative Approach Towards Image Classification. International Journal of Innovative Research in Computer Science & Technology. 8(6). 411–415. 1 indexed citations
12.
Singh, Ajay Vikram, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, et al.. (2020). Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Advanced Intelligent Systems. 2(12). 34 indexed citations
13.
Singh, Ajay Vikram, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, et al.. (2020). Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Advanced Intelligent Systems. 2(12). 109 indexed citations
14.
Singh, Rishabh, et al.. (2020). AI And Precision Medicine For Oncology. SSRN Electronic Journal. 1 indexed citations
15.
Singh, Ajay Vikram, Anurag Kanase, Katherina Siewert, et al.. (2020). Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells. ACS Applied Materials & Interfaces. 13(1). 1943–1955. 124 indexed citations
16.
Singh, Rishabh & José C. Prı́ncipe. (2018). Correntropy Based Hierarchical Linear Dynamical System For Speech Recognition. 17. 1–7. 2 indexed citations
17.
Singh, Rishabh, et al.. (2013). Fungal Flora of Vermicompost and Organic Manure : A case Study of Molecular Diversity of Mucor racemosus using RAPD Analysis. Biosciences Biotechnology Research Asia. 10(2). 1 indexed citations
18.
19.
Singh, Surendra & Rishabh Singh. (1993). On the use of Levin's T-transform in accelerating the summation of series representing the free-space periodic Green's functions. IEEE Transactions on Microwave Theory and Techniques. 41(5). 884–886. 19 indexed citations
20.
Singh, Rishabh. (1957). The Green's function of an elliptic plate. Mathematika. 4(1). 61–69. 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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