Computational Intelligence Group

Research Projects

• ACERTA: Análisis Computacional de Estados de Riesgo en Trastornos Afectivos (Más info). Computational Analysis of Risk States in Affective Disorders (More info).

Recent Publications

• López, V., Llamocca, P., Cukic, M. (2025). Signal Encoding of a Piezoelectric Sensor Grid for Fuzzy Motor Activity Analysis in Mental Health. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2025. Lecture Notes in Networks and Systems, vol 1553. Springer, Cham. See
• Cotelo, C., López, V. (2025). Aportaciones de la inteligencia computacional a la mejora de la ética en las aplicaciones de la IA. Humanidades e Ciências Sociais: Perspectivas Teóricas, Metodológicas e de Investigação VIII. Editora Artemis 2025. DOI: 10.37572/EdArt_3103254755 See
• Llamocca, P., Guevara, C. Jiménez, Y. & López, V (2024) Smartwatches Should Facilitate the Aggregation of Mental Health Data. Society for industrial and applied mathematics (SIAM-2024) See
• Čukić, M., Olejarczyk,E., & Bachmann, M. (2024) Fractal analysis of electrophysiological signals to detect and monitor depression: what we know so far? Chapter 34 in Book ‘The Fractal Geometry of the Brain’_2nd Edition, by Prof. Antonio Di Ieva (Part of the book series: Advances in Neurobiology (NEUROBIOL, volume 36)). Springer Nature. DOI: 10.1007/978-3-031-47606-8_34 See
• Olejarczyk, E., Čukić, M., Porcaro, C., Zappasodi, F., & Tecchio, F. (2024) Clinical sensitivity of fractal neurodynamics. Chapter 3 in Book The Fractal Geometry of the Brain_2nd Edition, by Prof. Antonio Di Ieva (Part of the book series: Advances in Neurobiology (NEUROBIOL, volume 36)). Springer Nature. See
• Čukić, M., Annaheim, S., Bahrami, F., Defraeye, T., De Nys, K., & M. Jörger (2024) Physics-based digital twin guided individual dosing of transdermal fentanyl in advanced cancer patients: a pilot study.(2024) A Protocol Paper. British Medical Journal Open (BMJ Open), doi:10.1136/bmjopen-2024-085296
• Čukić, M., Annaheim, S., & Rossi, R. M. (2024) An Early Dementia Risk Screening Approach for healthy aging citizens. Conference paper MetroInd 4.0 & IoT, Firenze 28-31 May 2024. See
• Hidalgo, R., López, V., & Urgelés, D. (2024). Voice-based mood recognition: an application to mental health. Intelligent Management Of Data And Information In Decision Making., 14, (pp. 41-48). See
• Hidalgo Aragón, R., Llamocca Portella, P. (2024). Causality between daytime motor activity and sleep Quality. XX Conferencia de la Asociación Española para la Inteligencia Artificial., (pp. 495-500). See
• Agudelo, Y. J., Espinosa, M., & Cukic, M. (2024). Cloud aggregation of sensor data: an application on mood disorder analysis, IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0 & IoT) (pp. 424-429). IEEE. See
• López, V., LLamoca, P., Mérida-Nicolich, A. (2024). A classification-based algorithm to characterize euthymia data in mental health, 19th International Conference on Soft Computing Models in Industrial and Environmental Applications, Salamanca, Spain.
• Cotelo, C., López, V. (2024). Contributions of Computational Intelligence to Enhancing Ethics in AI Application, XXXI International European Business Ethics Network (EBEN), Cáceres, Spain.
• Jiménez, Y., Espinosa, M., Lopez, V., Llamocca, P., Urgelés, D. & Guevara, C. (2023).Detection of Risk States in Emotional Disorders. 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2023), Fuzhou, China. See
• Lopez, V., Urgelés, D., Llamocca, P., Jiménez, Y., Espinosa, M., Lagunas, N. & Guevara, C. (2023) Data integration process without predetermined structure and its application in mental health. SEIO 2023, Elche, Spain. See
• Lopez, V., Llamocca, P., Urgelés, D., Jiménez, Y., Guevara, C., Viñals, C., & Espinosa, M. (2023). Verification of the integration process of algorithms and sensor data for mental health applications. Human Systems Engineering and Design (IHSED 2023): Future Trends and Applications, 112(112).
• Flora Bahrami, René Michel Rossi, Katelijne De Nys, Markus Jörger, Milena Čukić Radenkovic and Thijs Defraeye (2023) Implementing physics-based digital patient twins to tailor the shift/change of oral morphine to transdermal fentanyl patches based on patient physiology. European Journal of Pharmaceutical Sciences, Volume 195. See
• Čukić, M., & Galović, S. (2023) Improvement of Digital twin for transdermal fentanyl delivery based on anomalous diffusion. IAPCS-10 Belgrade 4-6 Sept 2023. ADMET & DMPK. See
• Čukić, M., Annaheim, S., Eggenberger, P. , & Rossi, R. M. (2023) Possible scalable solution for early detection of prodromal phase of dementia based on EEG complexity. Biomedical Signal Processing & Control on February 21. 2024. BSPC-D-24-01384 Preprint at Science Square See
• Čukić, M., & López, V. (2022). Progress in Objective Detection of Depression and Online Monitoring of Patients Based on Physiological Complexity. Frontiers in Psychiatry, 13, 828773. See
• Čukić, M. B., Llamocca, P., & Lopez, V. (2022). An Unexpected Connection from Our Personalized Medicine Approach to Bipolar Depression Forecasting. In Proceedings of SAI Intelligent Systems Conference (pp. 226-235). Cham: Springer International Publishing. See
• Llamocca, P., López, V., & Čukić, M. (2022). The proposition for bipolar depression forecasting based on wearable data collection. Frontiers in Physiology, 12, 777137. See
• Llamocca, P., López, V., Santos, M., & Čukić, M. (2021). Personalized characterization of emotional states in patients with bipolar disorder. Mathematics, 9(11), 1174. See
• López, V., & Čukić, M. (2021). A dynamical model of SARS-CoV-2 based on people flow networks. Safety Science, 134, 105034. See
• Datta, S. P. A., Saleem, T. J., Barati, M., López, M. V. L., Furgala, M. L., Vanegas, D. C., … & McLamore, E. S. (2021). Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3). Big data analytics for Internet of Things, 7-88. See
• Čukić, M., Pokrajac, D., & Lopez, V. (2021). On mistakes we made in prior computational psychiatry data driven approach projects and how they jeopardize translation of those findings in clinical practice. In Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3 (pp. 493-510). Springer International Publishing. See
• Čukić, M., López, V., & Pavón, J. (2020). Classification of depression through resting-state electroencephalogram as a novel practice in psychiatry. Journal of medical Internet research, 22(11), e19548. See
• Llamocca, P., Urgelés, D., Cukic, M., & Lopez, V. (2019, November). Bip4Cast: Some advances in mood disorders data analysis. In Proceedings of the 1st International Alan Turing Conference on Decision Support and Recommender Systems, London. See
• Setti Alonso, I., & Pinto Lozano, J. M. (2019). Análisis de datos útiles en predicción de trastornos emocionales. See
• Portella, P. L., Čukić, M., Junestrand, A., Urgelés, D., & López, V. L. (2018). Data source analysis in mood disorder research. In XVIII Conferencia de la Asociación Espanola para la Inteligencia Artificial (CAEPIA 2018) (pp. 23-26). See
• Anchiraico Trujillo, J. C. (2017). Diseño de una arquitectura Big Data para la predicción de crisis en el trastorno bipolar. See

Members

Victoria López

Director of the Polytechnic School of CUNEF University. She holds a PhD in Computational Mathematics and Artificial Intelligence and she has been a researcher at University of Amsterdam and the Complutense University of Madrid. Her interests include Computational Mathematics and Artificial Intelligence.

Diego Urgelés

Psychiatrist specialized in the treatment of bipolar disorder, depression and addictions practicing in Madrid since 2002. Completed his residency in psychiatry in Madrid, at the Hospital de La Princesa and holds a PhD. in research in medical-surgical sciences by the Universidad Complutense of Madrid.

María Espinosa

PhD candidate in Natural Language Processing (NLP) at UNED, Spain. Computer Scientist and English Studies bachelor. Research interests: NLP, ML, Linguistics, and Information Verification.

Diego Riofrío

Diego Fernando Riofrío focuses his research on human-computer interaction, virtual reality, adaptive systems for education, and educational data mining. His most significant work includes developing intelligent tutoring systems for procedural training and using data science to model student behavior. He has authored numerous papers on these topics, with research contributions recognized in leading journals. His teaching career spans several years, covering subjects like advanced app programming, database management, software usability, and computer science theory, consistently teaching from 2017 to the present across various levels of higher education.

Milena Čukić

Bachelor with a Master in Engineering of Biomedical Electronics, with Magisterium in Biophysics and PhD in Neuroscience; specialized in fractal and nonlinear analysis of electrophysiological signals (neurophysiology). Focus of research at early prediction of psychiatric and neurological disorders (physiological complexity), as well as Digital Health and Active Inference.

Pavél Llamocca

PhD in Computer Science from the Complutense University of Madrid. Work experience for more than 10 years in consulting in banking and financial entities. His research interests include Machine Learning techniques applied to mental health, Smart Cities, and Open Data.

César Guevara

Graduated in information systems and sciences from the Universidad de las Fuerzas Armadas, Ecuador, a master’s degree in computer science and Ph.D. in computer engineering from the Complutense University of Madrid. His research interests include the application of artificial intelligence to detect human behavior and cybersecurity.

Miguel Andrés

PhD candidate at Complutense University of Madrid, Spain. Computer bachelor. Research interests: Video games, VR, Agile methodologies.

Roberto Morales

Roberto Morales is a professor in the Department of Quantitative Methods at CUNEF since 2010. He holds a PhD in Econometrics with honors from the University of Alcalá and began his academic career at UC3M. He has taught in Master’s programs at the University of Alcalá and UC3M. In 2024, he was accredited as a European Statistician by FENStatS and previously received the Complutense Award for one of the top academic records in Social Sciences.

Carlos Cotelo

Carlos Cotelo is a full-time professor at CUNEF. He has a degree in Philosophy and a PhD in Journalism. He has a teaching career of more than twenty years, combined with professional experience in sectors related to Marketing and audiovisual production. His role in the group will be focused on ensuring that our experiments meet the necessary ethical requirements.

Mateo Pérez

PhD candidate in Psychologhy at UCM, Spain. Graduated in psychology with clinical and educational mention Research interests: Bipolar Disorder, Substance abuse, technology addictions. General health psychologist

External Collaborators

• Manuel Faraco (Centro Adalmed)

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