Leonardo Garrido

73 total papers · 1.1k total citations
34 papers, 422 citations indexed

About

Leonardo Garrido is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Leonardo Garrido has authored 34 papers receiving a total of 422 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Information Systems. Recurrent topics in Leonardo Garrido's work include Reinforcement Learning in Robotics (4 papers), Robotic Path Planning Algorithms (3 papers) and Modular Robots and Swarm Intelligence (3 papers). Leonardo Garrido is often cited by papers focused on Reinforcement Learning in Robotics (4 papers), Robotic Path Planning Algorithms (3 papers) and Modular Robots and Swarm Intelligence (3 papers). Leonardo Garrido collaborates with scholars based in Mexico, United States and Spain. Leonardo Garrido's co-authors include Ramón Brena, Katia Sycara, Héctor Toledo, Francisco J. Cantú-Ortiz, Hugo Terashima‐Marín, Alejandro González-García, Enrique Garcia-Ceja, Francisco Carneiro, Manuel Mora and Mariano Alcañíz and has published in prestigious journals such as Sensors, Computer Methods and Programs in Biomedicine and Control Engineering Practice.

In The Last Decade

Leonardo Garrido

30 papers receiving 393 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Leonardo Garrido	106	85	59	47	41	34	422
Guillermo Hernández	75 0.7×	22 0.3×	45 0.8×	64 1.4×	21 0.5×	48	386
Muhammad Azeem Akbar	98 0.9×	49 0.6×	77 1.3×	149 3.2×	62 1.5×	53	447
Jianru Xue	110 1.0×	117 1.4×	42 0.7×	30 0.6×	28 0.7×	20	425
Peter Novák	98 0.9×	184 2.2×	101 1.7×	19 0.4×	8 0.2×	28	471
Luiz Eduardo Galvão Martins	83 0.8×	86 1.0×	46 0.8×	148 3.1×	28 0.7×	53	456
M. Bhuvaneswari	60 0.6×	70 0.8×	21 0.4×	49 1.0×	21 0.5×	21	493
Jiao Yin	123 1.2×	28 0.3×	118 2.0×	106 2.3×	28 0.7×	34	477
A. A. Abd El-Aziz	91 0.9×	47 0.6×	51 0.9×	61 1.3×	29 0.7×	50	409
N. Parameswaran	112 1.1×	111 1.3×	130 2.2×	123 2.6×	43 1.0×	44	405
Lei Li	84 0.8×	28 0.3×	70 1.2×	39 0.8×	70 1.7×	60	376

Countries citing papers authored by Leonardo Garrido

Since Specialization

Citations

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

Fields of papers citing papers by Leonardo Garrido

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonardo Garrido

This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Garrido. A scholar is included among the top collaborators of Leonardo Garrido 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 Leonardo Garrido. Leonardo Garrido is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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