Juan Alejandro Peña Palacio

Resumen
Área de Gestión de la Información y Riesgos

Información general

Profesor Titular.

Contacto

Teléfono/phone: (57) 604 2619500 Ext 9208
Correo electrónico/e-mail: japena@eafit.edu.co
Dirección/address: Carrera 49 N 7 sur 50, Medellín-Colombia. Bloque 19 - Oficina 26-108

Resumen CV / Summary

Científico de datos, experto en modelamiento de riesgos y aprendizaje automático, y posee un Doctorado en Ingeniería con énfasis en modelamiento computacional utilizando conceptos de inteligencia computacional y aprendizaje profundo. El profesor Alejandro ha trabajado en diferentes proyectos de investigación relacionados con el modelamiento del riesgo derivado de las operaciones de las organizaciones en sectores como la Banca (FINTECH), los Seguros (INSURTECH) y la Agricultura de Precisión (Ag-Tech). Como investigador visitante en el IAI (Instituto de Inteligencia Artificial) de la Universidad DeMontfort de Leicester (Inglaterra), el profesor Alejandro aplicó el modelamiento de riesgos para el mejoramiento de la sostenibilidad ambiental y financiera de pequeñas y medianas empresas del sector del agro. A lo largo de su desempeño profesional, el profesor Alejandro ha trabajado como miembro del Consejo Nacional de Acreditación (CNA), y ha participado como Editor en Chief y revisor de diferentes journals en el campo del soft-computing, el modelamiento computacional, y el riesgo operacional.

Intereses académicos e investigativos / Research and Teaching Interest

Estudios realizados / Education

Pasantía Post-Doctoral en el Institute of Artificial Intelligence (IAI) de la Universidad de DeMontfort en Leicester, Inglaterra
Doctor en Ingeniería de la Universidad Pontificia Bolivariana
Master en Ingeniería de Sistemas de la Universidad Nacional de Colombia
Ingeniero Mecánico de la Universidad Nacional de Colombia

Publicaciones / Publications

Peña, P. A. Tejada, J. González-Ruiz, J.D. Sepulveda, L. Chiclana, F. Góngora, M. (2023) An Evolutionary Intelligent Control System for a Flexible Joints Robot, Applied Soft Computing (135) (https://doi.org/10.1016/j.asoc.2023.110043).
González-Ruiz, J.D. Botero-Botero, S. Peña, A. (2022) Analysis of the Capital Structure in Sustainable Infrastructure Systems: A Methodological Approach, Sustainability 14(19), EISSN: 2071-1050, (https://doi.org/10.3390/su141912662)
Peña, A. Tejada, J. Gonzalez-Ruiz, J. Góngora, M. (2022) Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial Risk Approach, Sustainability 14 (11), EISSN: 2071-1050, (https://doi.org/10.3390/su14116668).
Toro-Ossaba, A. Jaramillo-Tigreros, J. Tejada, J.C. Peña, A. López-González, A. Castanho, R. (2022) LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals, Applied Sciences 12(19), EISSN 2076-3417, (https://doi.org/10.3390/app12199700).
Gabriela Ramirez, A. Monsalve, J. González-Ruiz,J.D. Almonacid, P. Peña, A. (2022) Relationship between the Cost of Capital and Environmental, Social, and Governance Scores: Evidence from Latin America, Sustainability 14(9), EISSN: 2071-1050, (https://doi.org/10.3390/su14095012).
Bonet, I, Peña, A., Lochmuller, Ch., Patiño, H., Chiclana, F., Góngora, M. (2021) Applying fuzzy scenarios for the measurement of operational risk, Applied Soft Computing (112) ISSN: 1568-49 (https://doi.org/10.1016/j.asoc.2021.107785).
Peña, A., Patiño, A., Chiclana, Carafinni, F., J. Góngora, M., F. González, Duque, E. (2021) Estimation of operational risk through the integration of multidimensional credibility concepts using a Fuzzy Convolutional Deep Learning Structure, Applied Soft Computing Journal (107), ISSN: 1568-4946 (https://doi.org/10.1016/j.asoc.2021.107381).
Henao, A. Panesso, C. Peña, A. Patiño, A. Vidal da Carvalho, J. (2021) Neural deep learning model to characterize the brand perception in insurance corporate advertising - Brand attributes to create travel insurance products based on sentiments, Smart Innovation, Systems and Innovation 209, Springer, Verlag. ISSN: 2190-3026 (https://doi.org/10.1007/978-981-33-4260-6_37).
Peña, A. Mesias, J. Patiño, A. Vidal da Carvalho, J. Gómez, G. Ibarra, K. Bedoya, S. (2021) PANAS-TDL: A Psychrometric Deep Learning Model for Characterizing Sentiments of Tourists against the COVID-19 pandemic on Twitter, Smart Innovation, Systems and Innovation 209, Springer, Verlag. ISSN: 2190-3026 (https://doi.org/10.1007/978-981-33-4260-6_15).
Bonet, I. Peña, A. Lochmueller, Ch. Patino, A. Góngora, M. (2021) Deep Clustering for Metagenomics, Computational Intelligence Methods for Bioinformatics and Biostatistics, in: Lecture Notes in Bioinformatics, Lecture Notes in Computer Science, vol. 12313, pp. 335-347 Springer Verlag. ISSN: 0302-9743 (https://doi.org/10.1007/978-3-030-63061-4_29).
Carvalho, J. V., Abreu, A., Peña, A., Ojeda, J. C. G., Liberato, D., & Liberato, P. (2020). Turismo, tecnologias e sistemas. [Tourism, Technologies and Systems] Revista Ibérica De Sistemas e Tecnologias De Informação, XI-XII (link).
González, J. Peña P., A. Duque, E. Chiclana, F. Góngora, M. (2019) Stochastic Logistic Fuzzy Maps for the Development of Integrated Multivariables Scenarios in the Financing of Infrastructure Projects, Applied Soft Computing, Vol. 85, Elsevier. (https://doi.org/10.1016/j.asoc.2019.105818).
Peña P., A. Bonet, I. Lochmueller, Ch. Tabares, M. Piedrahíta C. Sánchez, C. Giraldo, L. Góngora, M. Chiclana, F. (2018) A Fuzzy ELECTRE structured methodology to assess big data maturity in healthcare SME's, Soft Computing Journal, Springer Verlag (https://link.springer.com/article/10.1007%2Fs00500-018-3625-8).
Peña P., A. Bonet, I. Lochmueller, C. Chiclana, F. Góngora, M. (2018) Fuzzy credibility model to estimate the operational value at risk using endogenous and exogenous databases of risk events. Knowledge-Based Systems 159, Elsevier, (https://doi.org/10.1016/j.knosys.2018.06.007).
Peña P., A. Bonet, I. Lochmueller, C. Chiclana, F. Gongora, M. (2018) Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management, Applied Soft Computing Journal 65, 614-631. Elsevier, (https://doi.org/10.1016/j.asoc.2018.01.024).
Peña P., A. Bonet, I. Lochmueller, C. Chiclana, F. Góngora, (2018) M. An integrated inverse adaptive neural fuzzy system with MonteCarlo Structure sampling method for operational risk management, Experts Systems with Applications 98, 2018, 11-26. Elsevier, (https://doi.org/10.1016/j.eswa.2018.01.001).
Peña P., A. Bonet, I. Lochmueller, Ch. Tabares, M. Piedrahíta C. Sánchez, C. Giraldo, L. Góngora, M. Chiclana, F. (2018) A Fuzzy ELECTRE structured methodology to assess big data maturity in healthcare SME's, Soft Computing Journal, Springer Verlag (https://link.springer.com/article/10.1007%2Fs00500-018-3625-8).
Peña P., A. Bonet, I. Lochmueller, C. Chiclana, F. Góngora, M. (2018) Fuzzy credibility model to estimate the operational value at risk using endogenous and exogenous databases of risk events. Knowledge-Based Systems 159, Elsevier, (https://doi.org/10.1016/j.knosys.2018.06.007).
Peña P., A. Bonet, I. Lochmueller, C. Chiclana, F. Gongora, M. (2018) Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management, Applied Soft Computing Journal 65, 614-631. Elsevier, (https://doi.org/10.1016/j.asoc.2018.01.024).
Peña P., A. Bonet, I. Lochmueller, C. Chiclana, F. Góngora, (2018) M. An integrated inverse adaptive neural fuzzy system with MonteCarlo Structure sampling method for operational risk management, Experts Systems with Applications 98, 2018, 11-26. Elsevier, (https://doi.org/10.1016/j.eswa.2018.01.001).
Sánchez, C. Giraldo, L. Piedrahita, C. Bonet, I. Lochmueller, Ch. Tabares, M. Peña, P.A. (2018) Evaluation of models of decision trees and K-means models in the characterization or diagnosis of some diseases, Espacios Journal 39(28), ISSN: 0798-1015 – (http://www.revistaespacios.com/a18v39n28/a18v39n28p21.pdf).

Última modificación: 30/08/2023 16:00

Docentes e investigadores EAFIT Juan Alejandro Peña Palacio // <![CDATA[ _spBodyOnLoadFunctionNames.push("setupPageDescriptionCallout"); // ]]>