Ravi Madduri

3.4k total citations
78 papers, 1.2k citations indexed

About

Ravi Madduri is a scholar working on Information Systems and Management, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Ravi Madduri has authored 78 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Information Systems and Management, 35 papers in Computer Networks and Communications and 21 papers in Information Systems. Recurrent topics in Ravi Madduri's work include Scientific Computing and Data Management (39 papers), Distributed and Parallel Computing Systems (33 papers) and Advanced Data Storage Technologies (14 papers). Ravi Madduri is often cited by papers focused on Scientific Computing and Data Management (39 papers), Distributed and Parallel Computing Systems (33 papers) and Advanced Data Storage Technologies (14 papers). Ravi Madduri collaborates with scholars based in United States, New Zealand and United Kingdom. Ravi Madduri's co-authors include Ian Foster, Kyle Chard, Łukasz Łaciński, Álex Rodríguez, Wei Tan, Dinanath Sulakhe, Ryan Chard, Bo Liu, Carl Kesselman and Carole Goble and has published in prestigious journals such as Bioinformatics, PLoS ONE and Biophysical Journal.

In The Last Decade

Ravi Madduri

73 papers receiving 1.2k citations

Peers

Ravi Madduri
Joel Saltz United States
Phillip Lord United Kingdom
Stian Soiland‐Reyes United Kingdom
Adriane Chapman United Kingdom
Matthew Pocock United Kingdom
Ian Dunlop United Kingdom
Matthew Addis United Kingdom
Werner Dubitzky United Kingdom
Paolo Missier United Kingdom
Tudor Groza Australia
Joel Saltz United States
Ravi Madduri
Citations per year, relative to Ravi Madduri Ravi Madduri (= 1×) peers Joel Saltz

Countries citing papers authored by Ravi Madduri

Since Specialization
Citations

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

Fields of papers citing papers by Ravi Madduri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ravi Madduri

This figure shows the co-authorship network connecting the top 25 collaborators of Ravi Madduri. A scholar is included among the top collaborators of Ravi Madduri 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 Ravi Madduri. Ravi Madduri 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.
Zhou, Yichao, Lisha Zhu, Hyunki Kim, et al.. (2025). scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework. Cell Genomics. 5(5). 100875–100875.
2.
Hua, Xing, et al.. (2025). Evaluating multi-ancestry genome-wide association methods: Statistical power, population structure, and practical implications. The American Journal of Human Genetics. 112(10). 2493–2508. 1 indexed citations
3.
Justice, Amy C., Janet P. Tate, J. Michael Gaziano, et al.. (2024). Adaption and National Validation of a Tool for Predicting Mortality from Other Causes Among Men with Nonmetastatic Prostate Cancer. European Urology Oncology. 7(4). 923–932. 3 indexed citations
4.
Byna, Suren, et al.. (2024). AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI. eScholarship (California Digital Library). 1–12. 7 indexed citations
5.
Justice, Amy C., Benjamin H. McMahon, Ravi Madduri, et al.. (2024). A landmark federal interagency collaboration to promote data science in health care: Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now. JAMIA Open. 7(4). ooae126–ooae126. 1 indexed citations
6.
Klarqvist, Marcus D. R., Miao Li, Kibaek Kim, et al.. (2024). Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx. Computational and Structural Biotechnology Journal. 28. 29–39. 4 indexed citations
7.
Carrillo‐Pérez, Francisco, Marija Pizurica, Yuanning Zheng, et al.. (2024). Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models. Nature Biomedical Engineering. 9(3). 320–332. 11 indexed citations
8.
Li, Hui, et al.. (2023). User experience evaluation for MIDRC AI interface. 2–2. 1 indexed citations
9.
Vassy, Jason L., Daniel Posner, Yuk‐Lam Ho, et al.. (2023). Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program. JAMA Cardiology. 8(6). 564–564. 19 indexed citations
10.
Madduri, Ravi, Kyle Chard, Mike D’Arcy, et al.. (2019). Reproducible big data science: A case study in continuous FAIRness. PLoS ONE. 14(4). e0213013–e0213013. 23 indexed citations
11.
Ebenezer, David L., Panfeng Fu, Mark Maienschein‐Cline, et al.. (2019). Genetic deletion of Sphk2 confers protection against Pseudomonas aeruginosa mediated differential expression of genes related to virulent infection and inflammation in mouse lung. BMC Genomics. 20(1). 984–984. 16 indexed citations
12.
Jagodnik, Kathleen M., Simon Koplev, Sherry L. Jenkins, et al.. (2017). Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop. Journal of Biomedical Informatics. 71. 49–57. 22 indexed citations
13.
Al-khersan, Hasenin, Kaanan P. Shah, Segun Jung, et al.. (2017). A novel MERTK mutation causing retinitis pigmentosa. Graefe s Archive for Clinical and Experimental Ophthalmology. 255(8). 1613–1619. 17 indexed citations
14.
Walker, Mark, Ravi Madduri, Álex Rodríguez, Joseph L. Greenstein, & Raimond L. Winslow. (2016). Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models. Biophysical Journal. 110(5). 1038–1043. 5 indexed citations
15.
Chard, Kyle, Mike D’Arcy, Ben Heavner, et al.. (2016). I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets. Research Explorer (The University of Manchester). 319–328. 28 indexed citations
16.
Liu, Bo, Ravi Madduri, Borja Sotomayor, et al.. (2014). Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses. Journal of Biomedical Informatics. 49. 119–133. 50 indexed citations
17.
18.
Helmer, Karl G., José Luis Ambite, Rachana Ananthakrishnan, et al.. (2011). Enabling collaborative research using the Biomedical Informatics Research Network (BIRN). Journal of the American Medical Informatics Association. 18(4). 416–422. 38 indexed citations
19.
Tan, Wei, Ravi Madduri, Stian Soiland‐Reyes, et al.. (2010). CaGrid Workflow Toolkit: A taverna based workflow tool for cancer grid. BMC Bioinformatics. 11(1). 542–542. 26 indexed citations
20.
Laszewski, Gregor von, et al.. (2004). An overview of grid file transfer patterns and their implementation in the Java CoG kit. 12(3). 329–352. 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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