Natalia Díaz-Rodríguez

13.5k total citations · 4 hit papers
49 papers, 7.3k citations indexed

About

Natalia Díaz-Rodríguez is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Health Informatics. According to data from OpenAlex, Natalia Díaz-Rodríguez has authored 49 papers receiving a total of 7.3k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 6 papers in Health Informatics. Recurrent topics in Natalia Díaz-Rodríguez's work include Explainable Artificial Intelligence (XAI) (12 papers), Context-Aware Activity Recognition Systems (8 papers) and Adversarial Robustness in Machine Learning (6 papers). Natalia Díaz-Rodríguez is often cited by papers focused on Explainable Artificial Intelligence (XAI) (12 papers), Context-Aware Activity Recognition Systems (8 papers) and Adversarial Robustness in Machine Learning (6 papers). Natalia Díaz-Rodríguez collaborates with scholars based in Spain, France and Finland. Natalia Díaz-Rodríguez's co-authors include Javier Del Ser, Francisco Herrera, Adrien Bennetot, Raja Chatila, Siham Tabik, Sergio Gil-López, Daniel Molina, Salvador García, Alberto Barbado and Richard Benjamins and has published in prestigious journals such as European Journal of Operational Research, IEEE Access and Sensors.

In The Last Decade

Natalia Díaz-Rodríguez

47 papers receiving 7.0k citations

Hit Papers

Explainable Artificial Intelligence (XAI): Concepts, taxo... 2019 2026 2021 2023 2019 2023 2019 2023 1000 2.0k 3.0k 4.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
    2019 5.0k citations Natalia Díaz-Rodríguez, Javier Del Ser et al. Information Fusion profile →
  2. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
    2023 687 citations Sajid Ali, Tamer Abuhmed et al. Information Fusion profile →
  3. Continual Learning for Robotics: Definition, Framework, Learning\n Strategies, Opportunities and Challenges
    2019 288 citations Timothée Lesort, Vincenzo Lomonaco et al. arXiv (Cornell University) profile →
  4. Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation
    2023 287 citations Natalia Díaz-Rodríguez, Javier Del Ser et al. Information Fusion profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Natalia Díaz-Rodríguez Spain 19 4.2k 972 834 782 467 49 7.3k
Cynthia Rudin United States 38 5.2k 1.2× 859 0.9× 598 0.7× 728 0.9× 517 1.1× 172 10.1k
Siham Tabik Spain 27 3.8k 0.9× 719 0.7× 644 0.8× 1.3k 1.6× 392 0.8× 63 7.6k
Marco Túlio Ribeiro United States 13 6.4k 1.5× 711 0.7× 597 0.7× 1.1k 1.4× 804 1.7× 22 9.3k
Sergio Gil-López Spain 21 3.3k 0.8× 654 0.7× 635 0.8× 465 0.6× 345 0.7× 52 6.6k
Adrien Bennetot France 5 3.0k 0.7× 664 0.7× 637 0.8× 387 0.5× 322 0.7× 5 5.0k
Alberto Barbado Spain 3 3.0k 0.7× 656 0.7× 632 0.8× 377 0.5× 317 0.7× 5 5.0k
Richard Benjamins Spain 10 3.1k 0.7× 664 0.7× 658 0.8× 389 0.5× 413 0.9× 28 5.3k
Raja Chatila France 26 4.1k 1.0× 1.0k 1.0× 1.5k 1.8× 1.8k 2.3× 555 1.2× 102 9.2k
Andreas Holzinger Austria 54 3.8k 0.9× 1.4k 1.5× 551 0.7× 1.0k 1.3× 1.2k 2.6× 353 11.1k
Wojciech Samek Germany 35 5.6k 1.3× 906 0.9× 280 0.3× 1.9k 2.5× 323 0.7× 136 11.7k

Countries citing papers authored by Natalia Díaz-Rodríguez

Since Specialization
Citations

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

Fields of papers citing papers by Natalia Díaz-Rodríguez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Natalia Díaz-Rodríguez. 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 Natalia Díaz-Rodríguez. The network helps show where Natalia Díaz-Rodríguez may publish in the future.

Co-authorship network of co-authors of Natalia Díaz-Rodríguez

This figure shows the co-authorship network connecting the top 25 collaborators of Natalia Díaz-Rodríguez. A scholar is included among the top collaborators of Natalia Díaz-Rodríguez 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 Natalia Díaz-Rodríguez. Natalia Díaz-Rodríguez 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.
Díaz-Rodríguez, Natalia, et al.. (2025). On the disagreement problem in Human-in-the-Loop federated machine learning. Journal of Industrial Information Integration. 45. 100827–100827. 3 indexed citations
2.
Bennetot, Adrien, Ivan Donadello, Mauro Dragoni, et al.. (2024). A Practical Tutorial on Explainable AI Techniques. ACM Computing Surveys. 57(2). 1–44. 29 indexed citations
3.
Ali, Sajid, Tamer Abuhmed, Shaker El–Sappagh, et al.. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion. 99. 101805–101805. 687 indexed citations breakdown →
4.
Díaz-Rodríguez, Natalia, et al.. (2023). Gender and sex bias in COVID-19 epidemiological data through the lens of causality. Information Processing & Management. 60(3). 103276–103276. 13 indexed citations
5.
Bennetot, Adrien, et al.. (2022). Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI. Neural Networks. 155. 95–118. 13 indexed citations
6.
Díaz-Rodríguez, Natalia, A. Lamas, Gianni Franchi, et al.. (2022). EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case. arXiv (Cornell University). 62 indexed citations
7.
Trocan, Maria, et al.. (2022). Credit Risk Scoring Forecasting Using a Time Series Approach. Institutional Repository of the University of Granada (University of Granada). 16–16. 2 indexed citations
8.
Holzinger, Andreas, Matthias Dehmer, Frank Emmert‐Streib, et al.. (2021). Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Information Fusion. 79. 263–278. 145 indexed citations
9.
Díaz-Rodríguez, Natalia, Javier Del Ser, Adrien Bennetot, et al.. (2019). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion. 58. 82–115. 4972 indexed citations breakdown →
10.
Lesort, Timothée, Vincenzo Lomonaco, Andrei Stoian, et al.. (2019). Continual Learning for Robotics. arXiv (Cornell University). 10 indexed citations
11.
Lesort, Timothée, Vincenzo Lomonaco, Andrei Stoian, et al.. (2019). Continual Learning for Robotics: Definition, Framework, Learning\n Strategies, Opportunities and Challenges. arXiv (Cornell University). 288 indexed citations breakdown →
12.
Márquez, Alberto, et al.. (2019). Computing optimal shortcuts for networks. European Journal of Operational Research. 279(1). 26–37. 2 indexed citations
13.
Lesort, Timothée, et al.. (2018). State representation learning for control: An overview. Neural Networks. 108. 379–392. 145 indexed citations
14.
Díaz-Rodríguez, Natalia, et al.. (2017). Couch potato or gym addict? Semantic lifestyle profiling with wearables and knowledge graphs.. Neural Information Processing Systems. 3 indexed citations
15.
Díaz-Rodríguez, Natalia & Rodrigo I. Silveira. (2017). Implementing Data-Dependent Triangulations with Higher Order Delaunay Triangulations. ISPRS International Journal of Geo-Information. 6(12). 390–390. 2 indexed citations
16.
Pires, Ivan Miguel, Nuno M. García, Nuno Pombo, Francisco Flórez‐Revuelta, & Natalia Díaz-Rodríguez. (2016). Validation Techniques for Sensor Data in Mobile Health Applications. Journal of Sensors. 2016. 1–9. 29 indexed citations
17.
Díaz-Rodríguez, Natalia & Rodrigo I. Silveira. (2016). Implementing data-dependent triangulations with higher order Delaunay triangulations. QRU Quaderns de Recerca en Urbanisme. 1–10. 1 indexed citations
18.
Lilius, Johan, et al.. (2015). Registered Nurses’ Experiences with the Medication Administration Process. Institutional Repository of the University of Granada (University of Granada). 2015. 1–10. 13 indexed citations
19.
Lilius, Johan, et al.. (2014). . ACM Computing Surveys. 46(4). 3 indexed citations
20.

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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