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Juan Fernando González-Vergara

Research Engineer

Email: juan.gonzalez@sdas-group.com

Born in Quito, Ecuador in 1994, his high school specialization was in Physic and Mathematics and his undergraduate formation has been centered in the development of scientific investigation skills and hard sciences- Chemistry, Biology, Physics and Geology, with two courses approved for each plus all the Mathematics and engineering lectures of I.T. such as Advance Linear Algebra, Image Processing, HPC, AI, ML, Databases, Functional Programming, Software Engineer, among others. This approach has let him gain capabilities to successfully integrate the Software Industry, any Business Enterprise or the research community.

Research Interests

AI, DL,(un)supervised ML techniques to solve multidisciplinary problems, Big Data, Data Analitycs.

Publications (may take some time to be displayed)

generated by bibbase.org
  2021 (1)
A Support Vector Machine Implementation for Traffic Assignment Problem. González-Vergara, J.; Serrano, N.; and Iza, C. In Aguilar-Igartua, M.; Lassous, I. G.; de la Cruz Llopis, L. J.; and Begin, T., editor(s), PE-WASUN '21: Proceedings of the 18th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, Alicante, Spain, November 22 - 26, 2021, pages 41–48, 2021. ACM
A Support Vector Machine Implementation for Traffic Assignment Problem [link]Paper   doi   link   bibtex   2 downloads  
  2020 (2)
A Data-Driven Approach for Automatic Classification of Extreme Precipitation Events: Preliminary Results. González-Vergara, J.; Escobar-González, D.; Chaglla-Aguagallo, D.; and Peluffo-Ordóñez, D. H. In Florez, H.; and Misra, S., editor(s), Applied Informatics - Third International Conference, ICAI 2020, Ota, Nigeria, October 29-31, 2020, Proceedings, volume 1277, of Communications in Computer and Information Science, pages 197–209, 2020. Springer
A Data-Driven Approach for Automatic Classification of Extreme Precipitation Events: Preliminary Results [link]Paper   doi   link   bibtex   12 downloads  
Inverse Data Visualization Framework (IDVF): Towards a Prior-Knowledge-Driven Data Visualization. Vélez-Falconí, M.; González-Vergara, J.; and Peluffo-Ordóñez, D. H. In Florez, H.; and Misra, S., editor(s), Applied Informatics - Third International Conference, ICAI 2020, Ota, Nigeria, October 29-31, 2020, Proceedings, volume 1277, of Communications in Computer and Information Science, pages 266–280, 2020. Springer
Inverse Data Visualization Framework (IDVF): Towards a Prior-Knowledge-Driven Data Visualization [link]Paper   doi   link   bibtex   3 downloads