Andrés E. Tomás

2.1k total citations
34 papers, 234 citations indexed

About

Andrés E. Tomás is a scholar working on Hardware and Architecture, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Andrés E. Tomás has authored 34 papers receiving a total of 234 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Hardware and Architecture, 13 papers in Computational Theory and Mathematics and 9 papers in Artificial Intelligence. Recurrent topics in Andrés E. Tomás's work include Parallel Computing and Optimization Techniques (13 papers), Matrix Theory and Algorithms (11 papers) and Electromagnetic Scattering and Analysis (4 papers). Andrés E. Tomás is often cited by papers focused on Parallel Computing and Optimization Techniques (13 papers), Matrix Theory and Algorithms (11 papers) and Electromagnetic Scattering and Analysis (4 papers). Andrés E. Tomás collaborates with scholars based in Spain, United States and Germany. Andrés E. Tomás's co-authors include Vicente Hernández, José E. Román, Enrique S. Quintana–Ort́ı, A. Cristiano I. Malossi, Dimitrios S. Nikolopoulos, Pietro Manzoni, Anca Molnos, Juan‐Carlos Cano, Giuseppe Tagliavini and Éric Flamand and has published in prestigious journals such as Sensors, BMC Bioinformatics and Information Sciences.

In The Last Decade

Andrés E. Tomás

33 papers receiving 227 citations

Peers

Andrés E. Tomás

CTM

CNC

EEE

AMPO

Jonathan Hogg United Kingdom

Hasitha Muthumala Waidyasooriya Japan

Christian Deppe Germany

Roy Williams United States

Russell Bradford United Kingdom

Matthias Weber Germany

Padma Raghavan United States

S. Natarajan India

Christof Vömel United States

Jonathan Hogg United Kingdom

Andrés E. Tomás

161 ×2.2

CTM

94 ×1.5

52 ×0.9

CNC

23 ×0.4

EEE

34 ×1.0

AMPO

Citations per year, relative to Andrés E. Tomás Andrés E. Tomás (= 1×) peers Jonathan Hogg

Countries citing papers authored by Andrés E. Tomás

Since Specialization

Citations

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

Fields of papers citing papers by Andrés E. Tomás

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrés E. Tomás. 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 Andrés E. Tomás. The network helps show where Andrés E. Tomás may publish in the future.

Co-authorship network of co-authors of Andrés E. Tomás

This figure shows the co-authorship network connecting the top 25 collaborators of Andrés E. Tomás. A scholar is included among the top collaborators of Andrés E. Tomás 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 Andrés E. Tomás. Andrés E. Tomás 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

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

Tomás, Andrés E., Enrique S. Quintana–Ort́ı, & Hartwig Anzt. (2023). Fast truncated SVD of sparse and dense matrices on graphics processors. The International Journal of High Performance Computing Applications. 37(3-4). 380–393.

Aliaga, José I., Hartwig Anzt, Enrique S. Quintana–Ort́ı, & Andrés E. Tomás. (2023). Sparse matrix‐vector and matrix‐multivector products for the truncated SVD on graphics processors. Concurrency and Computation Practice and Experience. 35(28). 1 indexed citations

Dolz, Manuel F., et al.. (2023). Performance–energy trade-offs of deep learning convolution algorithms on ARM processors. The Journal of Supercomputing. 79(9). 9819–9836. 3 indexed citations

Aliaga, José I., et al.. (2022). Compressed basis GMRES on high-performance graphics processing units. The International Journal of High Performance Computing Applications. 37(2). 82–100. 7 indexed citations

Castelló, Adrián, et al.. (2022). High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS. Journal of Systems Architecture. 125. 102459–102459. 6 indexed citations

Barrachina, Sergio, Adrián Castelló, Manuel F. Dolz, et al.. (2022). Reformulating the direct convolution for high-performance deep learning inference on ARM processors. Journal of Systems Architecture. 135. 102806–102806. 11 indexed citations

Aliaga, José I., et al.. (2021). Compression and load balancing for efficient sparse matrix‐vector product on multicore processors and graphics processing units. Concurrency and Computation Practice and Experience. 34(14). 11 indexed citations

Catalán, Sandra, et al.. (2018). Look-ahead in the two-sided reduction to compact band forms for symmetric eigenvalue problems and the SVD. Numerical Algorithms. 80(2). 635–660. 1 indexed citations

10.

Malossi, A. Cristiano I., Michael Schaffner, Anca Molnos, et al.. (2018). The transprecision computing paradigm: Concept, design, and applications. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1105–1110. 45 indexed citations

11.

Hernández‐Orallo, Enrique, Andrés E. Tomás, Carlos T. Calafate, et al.. (2017). Evaluating the use of sub-gigahertz wireless technologies to improve message delivery in opportunistic networks. 305–310. 12 indexed citations

12.

Anzt, Hartwig, Jack Dongarra, Goran Flegar, Enrique S. Quintana–Ort́ı, & Andrés E. Tomás. (2017). Variable-Size Batched Gauss-Huard for Block-Jacobi Preconditioning. Procedia Computer Science. 108. 1783–1792. 4 indexed citations

13.

Hernández‐Orallo, Enrique, et al.. (2016). Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications. Sensors. 16(9). 1523–1523. 8 indexed citations

14.

Torres, José Salavert, Andrés E. Tomás, Ignacio J. Blanco, Kunihiko Sadakane, & Ignácio Blanquer. (2016). Pair-End Inexact Mapping on Hybrid GPU Environments and Out-Of-Core Indexes. Current Bioinformatics. 11(4). 459–469. 1 indexed citations

15.

Torres, José Salavert, Andrés E. Tomás, Joaquín Tárraga, et al.. (2015). Fast inexact mapping using advanced tree exploration on backward search methods. BMC Bioinformatics. 16(1). 18–18. 2 indexed citations

16.

Tomás, Andrés E., et al.. (2015). MuffinEc: Error correction for de Novo assembly via greedy partitioning and sequence alignment. Information Sciences. 329. 206–219. 4 indexed citations

17.

Tomás, Andrés E., Chia‐Chen Chang, Richard T. Scalettar, & Zhaojun Bai. (2012). Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems. 308–319. 5 indexed citations

18.

Hernández, Vicente, José E. Román, & Andrés E. Tomás. (2007). A robust and efficient parallel SVD solver based on restarted Lanczos bidiagonalization.. 31. 68–85. 22 indexed citations

19.

Hernández, Vicente, José E. Román, & Andrés E. Tomás. (2005). A Parallel Variant of the Gram-Schmidt Process with Reorthogonalization.. 25(9). 221–228. 3 indexed citations

20.

López, M.C., Susana Correa Garcı́a, José Ruíz-Herrera, & Andrés E. Tomás. (1997). The ornithine decarboxylase gene from Candida albicans. Sequence analysis and expression during dimorphism. Current Genetics. 32(2). 108–114. 16 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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