Rosa M. Badía

7.8k total citations · 1 hit paper
174 papers, 3.2k citations indexed

About

Rosa M. Badía is a scholar working on Computer Networks and Communications, Hardware and Architecture and Information Systems. According to data from OpenAlex, Rosa M. Badía has authored 174 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 131 papers in Computer Networks and Communications, 98 papers in Hardware and Architecture and 76 papers in Information Systems. Recurrent topics in Rosa M. Badía's work include Distributed and Parallel Computing Systems (94 papers), Parallel Computing and Optimization Techniques (93 papers) and Cloud Computing and Resource Management (63 papers). Rosa M. Badía is often cited by papers focused on Distributed and Parallel Computing Systems (94 papers), Parallel Computing and Optimization Techniques (93 papers) and Cloud Computing and Resource Management (63 papers). Rosa M. Badía collaborates with scholars based in Spain, United States and Germany. Rosa M. Badía's co-authors include Jesús Labarta, J. M. PÉREZ, Eduard Ayguadé, Juanjo Noguera, Jorge Ejarque, Judit Planas, Pieter Bellens, Xavier Martorell, Alejandro Durán and Raül Sirvent and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Computational Physics and European Journal of Biochemistry.

In The Last Decade

Rosa M. Badía

160 papers receiving 3.1k citations

Hit Papers

OmpSs: A PROPOSAL FOR PRO... 2011 2026 2016 2021 2011 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rosa M. Badía Spain 29 2.3k 2.0k 1.1k 257 228 174 3.2k
Jeremy Kepner United States 22 808 0.3× 488 0.2× 429 0.4× 106 0.4× 337 1.5× 82 1.9k
Rob V. van Nieuwpoort Netherlands 20 1.6k 0.7× 819 0.4× 715 0.6× 351 1.4× 101 0.4× 75 2.1k
Stephen L. Scott United States 27 1.5k 0.7× 598 0.3× 889 0.8× 50 0.2× 862 3.8× 150 2.7k
Rajeev Thakur United States 34 4.1k 1.8× 2.9k 1.5× 794 0.7× 245 1.0× 13 0.1× 167 5.0k
Henri E. Bal Netherlands 33 3.6k 1.5× 2.3k 1.2× 1.0k 0.9× 269 1.0× 36 0.2× 246 4.4k
Todd Tannenbaum United States 12 2.3k 1.0× 870 0.4× 1.1k 1.0× 837 3.3× 21 0.1× 29 2.7k
Douglas Thain United States 25 2.5k 1.1× 694 0.3× 1.4k 1.2× 1.1k 4.1× 18 0.1× 153 3.2k
Thomas Sterling United States 19 1.0k 0.4× 752 0.4× 336 0.3× 83 0.3× 29 0.1× 107 1.6k
Jesús Labarta Spain 32 3.4k 1.5× 3.1k 1.6× 1.3k 1.2× 172 0.7× 7 0.0× 254 4.2k
Raymond Namyst France 16 1.4k 0.6× 1.3k 0.6× 613 0.5× 85 0.3× 7 0.0× 42 1.8k

Countries citing papers authored by Rosa M. Badía

Since Specialization
Citations

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

Fields of papers citing papers by Rosa M. Badía

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rosa M. Badía. 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 Rosa M. Badía. The network helps show where Rosa M. Badía may publish in the future.

Co-authorship network of co-authors of Rosa M. Badía

This figure shows the co-authorship network connecting the top 25 collaborators of Rosa M. Badía. A scholar is included among the top collaborators of Rosa M. Badía 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 Rosa M. Badía. Rosa M. Badía 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.
Conejero, Javier, et al.. (2025). Orchestrating Quantum-HPC Workflows with Distributed Quantum Circuit Cutting. QRU Quaderns de Recerca en Urbanisme. 1898–1906.
2.
Hernández, J.A., Riccardo Rossi, Stefan Boschert, et al.. (2025). Parallel reduced-order modeling for digital twins using high-performance computing workflows. Computers & Structures. 316. 107867–107867. 1 indexed citations
3.
Marozzo, Fabrizio, et al.. (2024). Block size estimation for data partitioning in HPC applications using machine learning techniques. Journal Of Big Data. 11(1). 1 indexed citations
4.
Bigot, Julien, et al.. (2024). Extreme-scale workflows: A perspective from the JLESC international community. Future Generation Computer Systems. 161. 502–513. 1 indexed citations
5.
Silva, Rafael Ferreira da, Rosa M. Badía, Deborah Bard, et al.. (2024). Frontiers in Scientific Workflows: Pervasive Integration With High-Performance Computing. Computer. 57(8). 36–44. 9 indexed citations
6.
Callaghan, S., et al.. (2024). A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California. Communications Earth & Environment. 5(1). 5 indexed citations
7.
Badía, Rosa M., et al.. (2023). Thermodynamics-informed neural network for recovering supercritical fluid thermophysical information from turbulent velocity data. International Journal of Thermofluids. 20. 100448–100448. 20 indexed citations
8.
Castro-Ginard, A., C. Jordi, X. Luri, et al.. (2020). Hunting for open clusters in Gaia DR2: 582 new OCs in the Galactic disc. Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona). 145 indexed citations
9.
Ejarque, Jorge, et al.. (2020). AutoParallel: Automatic parallelisation and distributed execution of affine loop nests in Python. The International Journal of High Performance Computing Applications. 34(6). 659–675. 2 indexed citations
10.
Moretó, Miquel, Marc Casas, Alejandro Rico, et al.. (2019). On the maturity of parallel applications for asymmetric multi-core processors. Journal of Parallel and Distributed Computing. 127. 105–115. 7 indexed citations
11.
Tejedor, Enric, J. Álvarez Cid-Fuentes, & Rosa M. Badía. (2016). Infrastructure-agnostic programming and interoperable execution in heterogeneous grids. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 35(4). 986–1004. 1 indexed citations
12.
Ejarque, Jorge, et al.. (2012). Service Orchestration on a Heterogeneous Cloud Federation. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 31(1). 45–60. 1 indexed citations
13.
Ejarque, Jorge, et al.. (2010). Semantic resource allocation with historical data based predictions. SZTAKI Publication Repository (Hungarian Academy of Sciences). 13 indexed citations
14.
Micsik, András, Jorge Ejarque, & Rosa M. Badía. (2010). A semantic toolkit for scheduling in cloud and grid platforms. SZTAKI Publication Repository (Hungarian Academy of Sciences). 2010. 30–31. 1 indexed citations
15.
Badía, Rosa M. & Nanbor Wang. (2009). Proceedings of the 2009 Workshop on Component-Based High Performance Computing. IEEE International Conference on High Performance Computing, Data, and Analytics. 2 indexed citations
16.
Casas, Marc, Rosa M. Badía, & Jesús Labarta. (2008). 2008 IEEE International Conference on Cluster Computing. 242–251. 1 indexed citations
17.
Casas, Marc, Rosa M. Badía, & Jesús Labarta. (2007). Automatic Phase Detection of MPI Applications.. 8(1). 129–136. 15 indexed citations
18.
Sadjadi, S. Masoud, et al.. (2007). Improving Separation of Concerns in the Development of Scientific Applications. Software Engineering and Knowledge Engineering. 456–461. 1 indexed citations
19.
Badía, Rosa M., Christian Pérez, Artur Andrzejak, & Álvaro Arenas. (2007). Grid and cluster computing. 359–359. 7 indexed citations
20.
Noguera, Juanjo & Rosa M. Badía. (2000). Run-time HW/SW codesign for discrete event systems using dynamically reconfigurable architectures. 100–106. 8 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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