Data Science Institute

Colegio de Administración y Economía

Acerca de nosotros

El Data Science Institute es una entidad sin personalidad jurídica que fomenta la investigación en todas las áreas de la ciencia de datos y sus aplicaciones para la resolución de problemas reales.

Escuela Académica

Business School

Intereses de investigación

Inteligencia artificial, machine learning, data analytics, big data, optimización, simulación, computación evolutiva, data mining, reconocimiento de patrones, control predictivo, investigación por encuestas, survey science, análisis del comportamiento.

Misión

Dar solución a problemas reales.

Visión

Ser el instituto líder en investigación y aplicación de data science a nivel nacional.

Investigación

En proceso:

Principal Component Analysis Formulations to Tackle Complex Real-world Optimization Problems
New gender-based crossover operator for single and multiobjective evolutionary optimisation
A new repair operator for constrained optimisation
Intelligent predictive data analysis techniques applied to dropping out of university studies
Respect Factor Research--The Respect Project--Latin America
Geographic, economic, and cultural predictors of health disparities
Evaluating the Quality of the SIPP and the ATUS Using Administrative Records and Paradata

Concluidos:

Incorporación de preferencias en el diseño automático de algoritmos para optimización multiobjetivo
Integrating Meta-Heuristics, Simulation and Exact Techniques for Production Planning of a Failure-Prone Manufacturing System
"Big Data Meets Survey Science" Special Issue
Exploring New Statistical Frontiers at the Intersection of Survey Science and Big Data: Convergence at “BigSurv18?”
Memory Gaps in the American Time Use Survey. Are Respondents Forgetful or is There More to it?
Capítulo "Calendar and time diary methods: tools to assess well-being in the 21st century" para libro Innovative Research Methods in Health Social Sciences

Publicaciones

Diaz, Juan Esteban, & López-Ibáñez, Manuel. 2020. Incorporating Decision-Maker's Preferences into the Automatic Configuration of Bi-Objective Optimisation Algorithms. European Journal of Operational Research. In press. https://doi.org/10.1016/j.ejor.2020.07.059
Diaz, Juan Esteban, & López-Ibáñez, Manuel. 2020. Incorporating Decision-Maker's Preferences into the Automatic Configuration of Bi-Objective Optimisation Algorithms. Zenodo. http://doi.org/10.5281/zenodo.3749288
Diaz, Juan Esteban., Handl, Julia., Xu, Dong-Ling, 2018. Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system. European Journal of Operational Research 266, 976–989. https://doi.org/10.1016/j.ejor.2017.10.062
Espinoza Aguirre, C. B., & Iñiguez Matute, P. F. (2018). Implementación de Gobierno de TI. Editorial USFQ, 6(1), 25. Peer Review.
C. Espinoza-Aguirre and D. Pillo, "IT governance model for public institutions with a focus on higher education," 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), Caceres, 2018, pp. 1-14, https://doi: 10.23919/CISTI.2018.8399248
Aguirre, C. B. E., & Iñiguez, P. F. (2018). Gobierno de Tecnologías de la Información (TI) como aliado del Negocio. Editorial Académica Española.
Hill, C. A., Biemer, P., Buskirk, T., Callegaro, M., Córdova-Cazar, A. L., Eck, A., Japec L., Kirchner A., Kolenikov S., Lyberg L., & Sturgis, P. (2019, April). Exploring New Statistical Frontiers at the Intersection of Survey Science and Big Data: Convergence at “BigSurv18”. In Survey Research Methods (Vol. 13, No. 1, pp. 123-134). http://dx.doi.org/10.18148/srm/2019.v1i1.7467
Eck, A., Córdova-Cazar, A.L., Callegaro, M., & Biemer, P. (2019). Big Data Meets Survey Science. Social Science Computer Review, 1, 5. https://doi-org.libproxy.unl.edu/10.1177/0894439319883393