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Jorge Maestre Vidal
PeerJ Reviewer
35 Points

Contributions by role

Reviewer 35

Contributions by subject area

Biogeography
Computational Biology
Plant Science
Data Mining and Machine Learning
Spatial and Geographic Information Science

Jorge Maestre Vidal

PeerJ Reviewer

Summary

Jorge Maestre Vidal received a Computer Science Engineering degree from the University Complutense of Madrid (Spain) in 2012. He holds a M.Sc. in Research in Computer Science from the University Complutense of Madrid in 2013. In 2016 he is Visiting Research at Instituto de Telecomunicações (IT), Aveiro, Portugal. He is currently a Ph.D. student at the University Complutense of Madrid and a member of the research group GASS (http://gass.ucm.es), in the Department of Software Engineering and Artificial Intelligence (DISIA) of the Faculty of Computer Science and Engineering. In addition to his academic activities, his professional experience includes projects with private organizations (Banco Santander, Safelayer Secure Communications, etc.) and public (FP7, Horizon 2020, Plan Nacional de I+D+i, Ministerio de Defensa, etc.). He is currently participant in the European projects SELFNET (H2020-ICT-2014-2/671672) and RAMSES (H2020-FCT-04-2015/700326). His main research interests are Artificial Intelligence, Pattern Recognition and Information Security.

Adaptive & Self-Organizing Systems Artificial Intelligence Computational Science Computer Networks & Communications Data Mining & Machine Learning Data Science Mobile & Ubiquitous Computing Network Science & Online Social Networks Operating Systems Scientific Computing & Simulation Security & Privacy Social Computing Software Engineering

Past or current institution affiliations

Universidad Complutense de Madrid

Work details

Researcher

Universidad Complutense de Madrid
Department of Software Engineering and Artificial Intelligence (DISIA)

Researcher

Complutense University of Madrid (UCM), Faculty of Computer Science and Engineering
Department of Software Engineering and Artificial Intelligence (DISIA)

Websites

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PeerJ Contributions

  • Reviewed 1

Signed reviews submitted for articles published in PeerJ Note that some articles may not have the review itself made public unless authors have made them open as well.

August 22, 2018
Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential
Tomislav Hengl, Markus G. Walsh, Jonathan Sanderman, Ichsani Wheeler, Sandy P. Harrison, Iain C. Prentice
https://doi.org/10.7717/peerj.5457 PubMed 30155360