Gabriela Marín Raventós

Gabriela Marín Raventós

Es estudiante: 

Formación académica

  • "Doctor of Philosophy" en Administración de Negocios, con énfasis en Sistemas de Información Gerencial, Graduate School of Business Administration, Texas A&M University, College Station, Texas, U.S.A., agosto 1993.
  • "Master of Science" en Ciencias de la Computación, Department of Computer Engineering and Science, Graduate School of Engineering, Case Western Reserve University, Cleveland, Ohio, U.S.A., agosto 1985.
  • Estudios de Maestría en Administración Pública, Programa Instituto Centroamericano de Administración Pública (ICAP) - Universidad de Costa Rica, San José, Costa Rica, de junio 1981 a abril 1982 (29 créditos).
  • Licenciatura en Ciencias de la Computación, Escuela de Matemáticas, Universidad de Costa Rica, San Pedro, Costa Rica, febrero 1981.
  • Bachillerato en Ciencias de la Computación, Escuela de Matemáticas, Universidad de Costa Rica, San Pedro, Costa Rica, agosto 1980.

Experiencia laboral

  • Directora, Centro de Investigación en Tecnologías de Información y Comunicación (CITIC), Universidad de Costa Rica,  21 de junio 2012 al 31 de julio 2017.
  • Decana del Sistema de Estudios de Posgrado, Universidad de Costa Rica, 30 junio 2008 al 29 junio 2012.
  • Vice-Decana del Sistema de Estudios de Posgrado, Universidad de Costa Rica, 14 de noviembre del 2007 al 29 de junio del 2008. 
  • Directora, Programa de Posgrado en Computación e Informática, Universidad de Costa Rica, 15 de mayo de 1998 al 30 de julio del 2009.
  • Representante, Area de Ingeniería ante el Cosejo del Sistema de Estudios de Posgradp, Universidad de Costa Rica, 2002-2004, 2004-2006, 2006-2008.
  • Profesora Catedrática, Escuela de Ciencia de la Computación e Informática, Universidad de Costa Rica, julio 1981- al presente.
  • Miembro, Comisión de Maestría, Telemática, 1998 hasta 2001.
  • Profesora y coordinadora, Programa de Diplomado en Computación Administrativa, Universidad de Costa Rica, desde 1987 hasta julio 1989.
  • Investigador I, Consejo Nacional de Rectores (CONARE), dese agosto 1980 a junio 1981.



Typifying Data Required for the Development of Smart Agriculture Systems


Smart agriculture is an active research field. Currently, many researchers are working on the construction of platforms directed to improve efficiency, crop processes, and data awareness. However, it is common that developers focus on data monitoring instead of the data relevance for decision making or the costs associated with the creation of monitoring platforms. In this paper, we present a classification of the data required by researchers on the construction of decision-support systems applied to smart agriculture processes. By using this classification, the user can define which data are relevant according to the characteristics of the problem that needs to be solved.We have applied the classification to data recollected in a study case conducted by the end of last year. Besides, we identify a list of agronomic and climatic variables commonly used in the construction of decision support systems. We apply the classification to this list of variables as an example for researchers. As a conclusion, this typification permits the researcher to identify data that has to be monitored and controlled, and data that does not have to be measured, the later based on the data characteristics and utility for the farmer.

Tipo de publicación: Conference Paper

Publicado en: 2019 IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture


The objective of this work is to detect Alzheimer’s disease using Magnetic Resonance Imaging. For this, we use a three-dimensional densenet-121 architecture. With the use of only freely available tools, we obtain good results: a deep neural network showing metrics of 87% accuracy, 87% sensitivity (micro-average), 88% specificity (micro-average), and 92% AUROC (micro-average) for the task of classifying five different classes (disease stages). The use of tools available for free means that this work can be replicated in developing countries.

Tipo de publicación: Book Chapter

Publicado en: Lecture Notes in Computer Science