Scholar
Ángel M. García-Vico
Google Scholar ID: utkISSsAAAAJ
Universidad de Jaén
Citations & Impact
Citations
279
H-index
8
i10-index
8
Publications
20
Co-authors
2
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Publications in international journals; Book chapters; Conferences; Other publications; Invited talks & seminars; Reviewer for international journals such as IEEE Access, Cognitive Computation, and Progress in Artificial Intelligence; Awards & distinctions including Best Student Award, Best work of initiation to research, Best research work, and Best paper.
Research Experience
- Post-doctoral researcher, University of Granada, Feb 2021 - Present; Postdoctoral researcher, University of Jaén, Apr 2020 - Jan 2021; PhD Student, University of Jaén, May 2017 - Apr 2020; PAS Contracted Labor, University of Jaén, December 2016 - April 2017; PAS Contracted Labor, University of Jaén, November 2015 - December 2015; Collaboration Scholarship of the Spanish Ministry of Education (Initiation to research), University of Jaén, November 2014 - June 2015.
Education
- Ph.D. in Computer Science with Cum Laude, University of Jaén, April 2020; MSc in Data Science and Computer Engineering, Specialization in Data Science and Intelligent Technologies, University of Granada, September 2016; Degree in Computer Engineering, Specialization in Intelligent Treatment of Information, University of Jaén, June 2015, including Best Student Award.
Background
- Ph.D. in Computer Science specialized in information systems and its intelligent treatment. Currently a member of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and a post-doc researcher at the Department of Computer Science and Artificial Intelligence, University of Granada. Main research interests include the extraction of descriptive rules through supervised learning using soft computing techniques in Big Data environments or data streams. Also interested in efficient deep learning techniques, specifically spiking neural networks.
