Jordi Nin
Assistant Professor, Department of Operations, Innovation and Data Sciences at ESADE
Contracted Doctoral Professor URL


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

Areas of interest

  • Financial networks analytics
  • Machine learning
  • Decision making and information fusion


Jordi Nin obtained the Computer Science Degree in 2004 from the Universitat Autònoma de Barcelona (UAB). Then he joined to the Artificial Intelligence Research Institute of Spanish National Research Council (IIIA-CSIC). In 2008 he received the Ph.D. in Computer Science obtaining the Outstanding Ph.D. Award from the UAB computer science department. Later he joined as Post-doctoral researcher at French National Center for Scientific Research (CNRS). In 2010, he joined the Computer Architecture Dept. of the Universitat Politècnica de Catalunya (UPC) as tenure-track lecturer. Lastly, from 2015 to 2019 he worked as senior data scientist at BBVA Data & Analytics.

Selected publications

Barja, A., Martínez, A., Arenas, A., Fleurquin, P., Nin, J., Ramasco, J. J. & et al. (2019). Assessing the risk of default propagation in interconnected sectoral financial networks. EPJ Data Science, 8 (Dec 2019), pp. 422-442. DOI: 10.1140/epjds/s13688-019-0211-y.

Nin, J. & Tomás, E. (2019). Default propagation in customer-supplier networks. Journal of Ambient Intelligence and Humanized Computing, 2 (6), pp. 541. DOI: 10.1007/s12652-019-01370-7.

Martínez, A., Nin, J., Tomás, E. & Rubio, A. (2019). Graph convolutional networks on customer/supplier graph data to improve default prediction. In Cornelius, S., Granell, C., Gómez-Gardenes, J. & Gonçalves, B. (Eds.), Complex Networks X (pp. 135-146). Berlin: Springer. DOI: 10.1007/978-3-030-14459-3_11.

Unceta Mendieta, Irene, Nin, J. & Pujol Borotau, J. (2019). Using copies to remove sensitive data: A case study on fair superhero alignment prediction. In Morales, A., Sánchez, J. & Ribeiro, B. (Eds.), Pattern recognition and image analysis. IbPRIA 2019. Lecture notes in computer science (pp. 182-193). Berlin: Springer. DOI: 10.1007/978-3-030-31332-6_16.

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: 10.1016/j.patrec.2017.08.024.

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: 10.1016/j.patrec.2016.08.010.

Nin, J., Nebot, À. & Binefa, X. (2016). Lessons learned about deep learning for credit card fraud scoring. In Nebot, À. & Binefa, X., Artificial intelligence research and development: Proceedings of the 19th International Conference of the Catalan Association for Artificial Intelligence (pp. 5-5). Amsterdam: IOS Press. DOI: 10.3233/978-1-61499-696-5-5.

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: 10.1016/j.knosys.2012.07.024.

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: 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: 10.1007/s10844-009-0112-9.