Enric Junqué de Fortuny

Assistant Professor of Information Systems and Business Analytics

Global Network Assistant Professor, NYU



Enric Junqué de Fortuny is an Assistant Professor of Information Systems and Business Analytics at NYU Shanghai. Prior to his appointment at NYU Shanghai, he was Assistant Professor in Marketing at the Rotterdam School of Management (Netherlands) and a Senior Research Fellow at INSEAD's eLab for Big Data (France/Singapore). He holds a Ph.D. in Applied Economics from the University of Antwerp, an M.Sc. in Computer Science Engineering, and a B.Sc. in Computer Science from the University of Ghent (Belgium).

His research interests lie within the realm of data science and its various applications. Specifically, he is focused on prediction problems involving fine-grained human behavior. His findings have been published in well-known journals and top conferences such as IEEE Transactions on Neural Networks, MISQ and Learning Systems and Knowledge Discovery and Data Mining (KDD).


Courses Taught

Information Technology in Business & Society


Recent Publications

Tobback, E., Daelemans, W, Junque de Fortuny, E., Naudts, H. & Martens, D. (2017). Belgian economic policy uncertainty index: improvement through text mining. International Journal of Forecasting, Accepted

Junque de Fortuny, E., De Smedt, T., Martens, D. & Daelemans, W (2016). Media coverage in times of political crisis: A text mining approach. Expert Systems with Applications, 39 (14), 11616-11622.

Martens, D., Junque de Fortuny, E., Clark, J. & Provost, F. (2016). Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics. MIS Quarterly, 40 (4), 869-888.

Junque de Fortuny, E., Evgeniou, T., Martens, D. & Provost, F. (2015). Iteratively refining SVMs using priors. In Proceedings of the IEEE International Conference on Big Data (pp. 46-52).

Junque de Fortuny, E. & Martens, D. (2015). Active Learning-based Pedagogical Rule Extraction. In IEEE Transactions on Neural Networks.

Junque de Fortuny, E., Stankova, A., Moeyersoms, J., Minnaert, B., Provost, F. & Martens, D. (2014). Corporate residence fraud detection. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1650-1659).

Junque de Fortuny, E., De Smedt, T., Martens, D. & Daelemans, W (2014). Evaluating and understanding text-based stock price prediction models. In Information Processing and Management.

Moeyersoms, J., Dejaeger, K., Junque de Fortuny, E., Baesens, B. & Martens, D. (2014). Comprehensible Software Fault and Effort Prediction: a Data Mining Approach. In Journal of Systems and Software.

Junque de Fortuny, E., Nian, T.T. & Provost, F. (2014). Revealing Life Events from Inferred Customer Similarity: A Predictive Modeling Approach. In Workshop on Information Technology and Systems (WITS).

Junque de Fortuny, E., Martens, D. & Provost, F. (2013). Predictive Modeling with Big Data: Is Bigger Really Better? In Big Data.

Junque de Fortuny, E. & Martens, D. (2012). Active learning based rule extraction for regression. In Data Mining Workshops (ICDMW) (pp. 926-933).

Hristoskova, A., Junque de Fortuny, E. & De Turck, F. (2011). Subsumption architecture for enabling strategic coordination of robot swarms in a gaming scenario. In Adaptive and Intelligent Systems (pp. 145-156).



Ph.D., Applied Economics
University of Antwerp

M.Sc., Computer Science Engineering
University of Ghent

B.Sc., Computer Science
University of Ghent 


Languages: Dutch, Catalan, English, French, Chinese (Basic)