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Junque de Fortuny, Enric

Major: Decision Sciences

Assistant Professor

Enric Junqué de Fortuny studies how artificial intelligence makes decisions, and how organizations should make decisions about it. He has trained more than 1,000 executives and business leaders through hands-on AI workshops, and was named by Poets&Quants as one of the World's Best 40 Under 40 MBA Professors.

His research started in data science, predicting fine-grained human behavior from large behavioral datasets, for which he was awarded the European Research Paper of the Year from the Association for Information Systems. It now centers on large language models as agents. He asks whether these models reason strategically in any real sense: how they form beliefs about other agents, evaluate their options, and choose. He also works on open-weight AI, and on the gap between models being released to everyone and models that most organizations can actually run.

 

Before SKKU he held faculty positions at IESE Business School, NYU Shanghai, and Rotterdam School of Management, and was a Senior Research Fellow at INSEAD's eLab for Big Data. He is an academic editor at INFORMS Service Science, and received a Meritorious Service Award from the INFORMS Journal of Data Science for his work on scientific reproducibility.

CONTACT INFORMATION

Publications
  • Junqué de Fortuny, E., De Smedt, T., Martens, D., & Daelemans, W. (2012). Media coverage in times of political crisis: A text mining approach. Expert Systems with Applications, 39(14), 11616–11622.
  • Junqué de Fortuny, E., Martens, D., & Provost, F. (2013). Predictive modeling with big data: Is bigger really better? Big Data, 1(4), 215–226.
  • Junqué de Fortuny, E. (2014a). Detecting patterns in human behavior and operationalization of predictive models. Proefschriften UA-TEW.
  • Junqué de Fortuny, E., De Smedt, T., Martens, D., & Daelemans, W. (2014b). Evaluating and understanding text-based stock price prediction models. Information Processing & Management, 50(2), 426–441.
  • Junqué de Fortuny, E., & Martens, D. (2015a). Active learning-based rule extraction. IEEE Transactions on Neural Networks and Learning Systems, 26, 2664–2677.
  • Tobback, E., Daelemans, W., Naudts, H., Junqué de Fortuny, E., & Martens, D. (2015b). Belgian economic policy uncertainty index: Improvement through text mining. International Journal of Forecasting, 34(2), 355–365.
  • Moeyersoms, J., Junqué de Fortuny, E., Dejaeger, K., Baesens, B., & Martens, D. (2015c). Comprehensible software fault and effort prediction: A data mining approach. Journal of Systems and Software, 100, 80–90.
  • Martens, D., Provost, F., Clark, J., & Junqué de Fortuny, E. (2016). Mining massive fine-grained behavior data to improve predictive analytics. MIS Quarterly, 40, 869–888.
  • Junqué de Fortuny, E., Martens, D., & Provost, F. (2018a). Wallenius Bayes. Machine Learning, 107, 1013–1037.
  • Evgeniou, T., Junqué de Fortuny, E., Nassuphis, N., & Vermaelen, T. (2018b). Volatility and the buyback anomaly. Journal of Corporate Finance, 49, 32–53.
  • Junqué de Fortuny, E., & Zhang, L. (2023). Exploring the new frontier: Decentralized financial services. Service Science, 15(4), 266–282.
  • Lee, J., & Junqué de Fortuny, E. (2023). Influencer-generated reference groups. Journal of Consumer Research, 49(1), 25–45.
  • Junqué de Fortuny, E., & Martens, D. (2012). Active learning-based rule extraction for regression. In 2012 IEEE 12th International Conference on Data Mining Workshops (pp. 926–933). https://doi.org/10.1109/ICDMW.2012.1
  • Junqué de Fortuny, E. (2014a). Detecting patterns in human behavior and operationalization of predictive models. Proefschriften UA-TEW.
  • Junqué de Fortuny, E., De Smedt, T., Martens, D., & Daelemans, W. (2014b). Evaluating and understanding text-based stock price prediction models. Information Processing & Management, 50(2), 426–441.
  • Junqué de Fortuny, E., Stankova, M., Moeyersoms, J., Minnaert, B., Provost, F., & Martens, D. (2014c). Corporate residence fraud detection. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1650–1659).
  • Nian, T., Junqué de Fortuny, E., & Provost, F. (2014d). Revealing life events from inferred customer similarity: A predictive modeling approach. In Proceedings of WITS 2014.
  • Tobback, E., Daelemans, W., Naudts, H., Junqué de Fortuny, E., & Martens, D. (2014e). Belgian economic policy uncertainty index: Improvement through text mining. In Workshop on Using Big Data for Forecasting and Statistics, EUROSYSTEM.
  • Junqué de Fortuny, E., & Martens, D. (2015). Active learning-based rule extraction. IEEE Transactions on Neural Networks and Learning Systems, 26, 2664–2677.
  • Junqué de Fortuny, E., Evgeniou, T., Martens, D., & Provost, F. (2015). Iteratively refining SVMs using priors. . In 2015 IEEE International Conference on Big Data (Big Data) (pp. 46–52).
  • Junqué de Fortuny, E. (2016). Social network models for marketing. In EMAC 2016.
  • Junqué de Fortuny, E. (2018). A quantum support vector machine circuit. In NYU–NYU Shanghai Data Science Summit.
  • Lee, J., & Junqué de Fortuny, E. (2020). Influencer-generated reference groups. In ACR Paris (virtual).
  • Lee, J., & Junqué de Fortuny, E. (2021). The power of words: How language shapes brand perceptions. In ACR (virtual).
  • Junqué de Fortuny, E. (2024a). Silicon sampling. Instituto de Ciencia de los Datos e Inteligencia Artificial, Universidad de Navarra.
  • Junqué de Fortuny, E. (2024b). Virtual personas: A hands-on journey into large language models. Arab Journal of Administrative Sciences.
  • Junqué de Fortuny, E. (2025). Simulating market equilibrium with large language models. In Proceedings of the 58th Hawaii International Conference on System Sciences (pp. 4976–4983). https://doi.org/10.24251/HICSS.2025.599
Awards & Honors

No awards registered.

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