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Jorge Casajús

Jorge Casajús

PhD Student

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After his graduation as a physicist by the Autonomous University of Madrid (UAM) in 2021, Jorge decided to switch career path and seek a more mathematical and data-oriented approach. As such, he got enrolled in the Artificial Intelligence MSc at the Technical University of Madrid (UPM). Here, he was granted the José Cuena award for his work during the MSc and, upon graduation, offered a position as an industrial PhD student. At the moment, he is researching AI applications to cybersecurity for Titanium Industrial Security S.L. (INZU Group) while collaborating with the Computational Intelligence Group at the UPM. Within a year of research work, he already published and presented in two international conferences, as well as made it to the final phase of the CajaMar University Hack Datathon 2023. He has also contributed to two cutting-edge cybersecurity projects in which Titanium Industrial Security S.L. is taking part alongside other cybersecurity companies.

  • Research Interests:
    • His research interest is oriented towards machine learning algorithms for anomaly detection in time series, focusing on their applicability to industrial control and cybersecurity. This is why he is also interested in harnessing the explainability properties of the algorithms he works with. Additionally, he has shown interest in data topology and graph theory for anomaly detection.
  • Publications
    • MsC Thesis
      • Casajús-Setién, Jorge. “Autocodificador evolutivo de red Bayesiana para detección de anomalías aplicado a ciberseguridad”. Trabajo de Fin de Master. E.T.S. de Ingenieros Informáticos (UPM), 2022.
    • Conference Proceedings
      • Casajús-Setién, Jorge, Bielza, Concha and Larrañaga, Pedro. “Evolutive adversarially-trained Bayesian network autoencoder for interpretable anomaly detection”. In: 11th International Conference on Probabilistic Graphical Models, 5-7 October 2022, p. 397-408.
      • Casajús-Setién, Jorge, Bielza, Concha and Larrañaga, Pedro. “Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models”. In: 2023 IEEE International Conference on Cyber Security and Resilience (CSR), p. 72-77.