Nin Guerrero, Jordi

Formación académica

• Doctor en Ciències de la Computació.
• Ingeniero en informática.
• Ingenieria técnica en informática de gestión.

Áreas de interés

• Análisis de redes financieras
• Aprendizaje automático
• Toma de decisiones y fusión de información

Trayectoria profesional

Publicaciones destacadas

• Nin, J. & Tomás, E. (2022). Default propagation in customer-supplier networks. Journal of Ambient Intelligence and Humanized Computing, 13, pp. 541. DOI: http://doi.org/10.1007/s12652-019-01370-7.
• Nin, J., Salbanya Rovira, Bernat, Fleurquin, P., Tomás, E., Arenas, A. & Ramasco, J. J. (2021). Modeling financial distress propagation on customer-supplier networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31 (5), 053119. DOI: http://doi.org/10.1063/5.0041104.
• Unceta Mendieta, Irene, Nin, J. & Pujol , O. (2020). Risk mitigation in algorithmic accountability: The role of machine learning copies. PLOS ONE, 15 (11), pp. e0241286-e0241302. DOI: http://doi.org/10.1371/journal.pone.0241286.
• Unceta Mendieta, Irene, Nin, J. & Pujol , O. (2020). Copying machine learning classifiers. IEEE Access, (8), pp. 160268-160284. DOI: http://doi.org/10.1109/ACCESS.2020.3020638.
• Barja, A., Martínez, A., Arenas, A., Fleurquin, P., Nin, J., Ramasco, J. J. & Tomás, E. (2019). Assessing the risk of default propagation in interconnected sectoral financial networks. EPJ Data Science, 8 (Dec 2019), pp. 422-442. DOI: http://doi.org/10.1140/epjds/s13688-019-0211-y.
• Gómez, J. A., Arévalo, J., Paredes, R. & Nin, J. (2018). End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognition Letters, 105 (1), pp. 175-181. DOI: http://doi.org/10.1016/j.patrec.2017.08.024.
• Martínez Rodríguez, D., Nin, J. & Nuñez-del-Prado, M. (2017). Towards the adaptation of SDC methods to stream mining. Computers & Security, 70 (1), pp. 702-722. DOI: http://doi.org/10.1016/j.cose.2017.08.011.
• Guttler, F., Ienco, D., Nin, J., Teisseire, M. & Poncelet, P. (2017). A graph-based approach to detect spatiotemporal dynamics in satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 130 (1), pp. 92-107. DOI: http://doi.org/10.1016/j.isprsjprs.2017.05.013.
• Capdevila, J., Cerquides, J., Nin, J. & Torres, J. (2017). Tweet-SCAN: An event discovery technique for geo-located tweets. Pattern Recognition Letters, 93 (1), pp. 58-68. DOI: http://doi.org/10.1016/j.patrec.2016.08.010.
• Herranz, J., Nin, J., Rodríguez-Marín Sastre, J. & Tassa, T. (2015). Revisiting distance-based record linkage for privacy-preserving release of statistical datasets. Data & Knowledge Engineering, 100 (1), pp. 78-93. DOI: http://doi.org/10.1016/j.datak.2015.07.009.
• Herranz, J. & Nin, J. (2014). Secure and efficient anonymization of distributed confidential databases. International Journal of Information Security, 13 (6), pp. 497-512. DOI: http://doi.org/10.1007/s10207-014-0237-x.
• Balasch-Masoliver, J., Muntés-Mulero, V. & Nin, J. (2014). Using genetic algorithms for attribute grouping in multivariate microaggregation. Intelligent Data Analysis, 18 (5), pp. 819-836. DOI: http://doi.org/10.3233/IDA-140670.
• Rebollo Monedero, D., Solé, M., Nin, J. & Forné, J. (2013). A modification of the k-means method for quasi-unsupervised learning. Knowledge-Based Systems, 37 (1), pp. 176-185. DOI: http://doi.org/10.1016/j.knosys.2012.07.024.
• Herranz, J., Nin, J. & Solé, M. (2012). More hybrid and secure protection of statistical datasets. IEEE Transactions on Dependable and Secure Computing, 9 (5), pp. 727-740. DOI: http://doi.org/10.1109/TDSC.2012.40.
• Herranz, J., Nin, J. & Solé, M. (2012). Kd-trees and the real disclosure risks of large statistical databases. Information Fusion, 13 (4), pp. 260-273. DOI: http://doi.org/10.1016/j.inffus.2011.03.001.
• Solé, M., Nin, J. & Muntés-Mulero, V. (2012). Efficient microaggregation techniques for large numerical data volumes. International Journal of Information Security, 11 (4), pp. 253-267. DOI: http://doi.org/10.1007/s10207-012-0158-5.
• Herranz, J., Nin, J. & Solé, M. (2011). Optimal symbol alignment distance: A new distance for sequences of symbols. IEEE Transactions on Knowledge and Data Engineering, 23 (10), pp. 1541-1554. DOI: http://doi.org/10.1109/TKDE.2010.190.
• Nin, J., Laurent, A. & Poncelet, P. (2010). Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach. Journal of Intelligent Information Systems, 35 (3), pp. 447-463. DOI: http://doi.org/10.1007/s10844-009-0112-9.
• Herranz, J., Matwin, S., Nin, J. & Torra, V. (2010). Classifying data from protected statistical datasets. Computers & Security, 29 (8), pp. 875-890. DOI: http://doi.org/10.1016/j.cose.2010.05.005.
• Nin, J. & Torra, V. (2009). Towards the evaluation of time series protection methods. Information Sciences: Informatics and Computer Science, 179 (11), pp. 1663-1677. DOI: http://doi.org/10.1016/j.ins.2009.01.024.
• Nin, J., Herranz, J. & Torra, V. (2008). On the disclosure risk of multivariate microaggregation. Data & Knowledge Engineering, 67 (3), pp. 399-412. DOI: http://doi.org/10.1016/j.datak.2008.06.014.
• Torra, V. & Nin, J. (2008). Record linkage for database integration using fuzzy integrals. International Journal of Intelligent Systems, 23 (6), pp. 715-734. DOI: http://doi.org/10.1002/int.20293.
• Nin, J., Herranz, J. & Torra, V. (2008). How to group attributes in multivariate microaggregation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16 (supp01), pp. 121-138. DOI: http://doi.org/10.1142/S0218488508005285.
• Nin, J., Herranz, J. & Torra, V. (2008). Rethinking rank swapping to decrease disclosure risk. Data & Knowledge Engineering, 64 (1), pp. 346-364. DOI: http://doi.org/10.1016/j.datak.2007.07.006.