(Spanish version)

Short Curriculum

Emilio Serrano is an associate professor at the Department of Artificial Intelligence at the Technical University of Madrid (UPM). He holds an M.Sc. Degree in Computer Science and a Ph.D. degree with European mention and Extraordinary Ph.D. Award in Artificial Intelligence, both from the University of Murcia. He has also been a visiting researcher at the University of Edinburgh, the University of Oxford, and the National Institute of Informatics in Tokyo. His scientific production includes over 80 publications, highlighting more than 25 papers in the JCR.  His main research line is the Social and Explainable Artificial Intelligence for Smart Cities. He lectures Deep Learning and Social Network Analysis among other courses; and, he has been principal investigator in three educational innovation projects in Data Science, having received two awards for educational innovation (2016 and 2021). He has also participated in several European and National funding programs such as FP7 research projects (smartopendata, eurosentiment, and omelette) and H2020 research projects (slidewiki and AI4EU).

Research interests

My research is focused on the field of Artificial Intelligence. Currently, I am very interested in: machine learning and deep learning; intelligent environments and ambient intelligence; agent-based social simulations and multi-agent systems; and, especially, the interaction among these fields to improve our quality of life. I take pleasure in using these technologies to solve interesting, complex, interdisciplinary, and difficult problems where the traditional computation techniques are not effective.

My main research line is the “Social and Explainable Artificial Intelligence for Smart Cities”. Social AI refers to the study and improvement of societies by AI technologies. These societies can be: human, artificial (including virtual agents as Google Assistant and physical agents like drones), or hybrid  (combinations of these). This combination of humans and distributed AI brings us to the main application domain of the research line: the Smart Cities. These urban areas use different types of electronic Internet of Things sensors to collect data and then use insights gained from that data to manage assets, resources and services efficiently. My research line aspires to an AI that not only drives our cars and recommends us series on Netflix, but also gives us strategies to achieve a more inclusive, fair and happy society (Social AI). Moreover, AI has to be capable of explaining these strategies so we can trust them (Explainable AI).

During my Ph.D. studies, I worked extensively on the analysis, design, and development of interaction methods among agents into an artificial society. The importance of multi-agent systems is that these systems may be used to implement a great variety of complex systems such as cloud computing, the Semantic Web, ambient intelligence, or social simulations.


  • European Ph.D. Degree in Computer Science. University of Murcia. 14/09/2011. (Extraordinary PhD Award in 2012). Title of the doctoral thesis: “Study And Development Of Methods And Tools For Testing, Validation And Verification Of Multi-Agent Systems.”
  • M.Phil. Degree in Information Technologies and Advanced Telematic. University of Murcia. 2007. (Quality award, Ref: MCD2006-00419) Title of thesis: “A platform for data mining in imperfect environments”.
  • M.S. Degree in Computer Science Engineering. University of Murcia. 2006. (Honourable Mention for Academic Excellence). Title of final project: “A prototype for modeling a system of cooperative metaheuristics: acquiring knowledge for the study of algorithm-instance problem”. (Level 3 (Máster) of the Spanish Higher Education Qualifications Framework (MECES) and level 7, of the European Qualifications Framework (EQF)).
  • B.S. Degree in Computer Science Engineering (Bologna process). University of Murcia. 2012
  • B.S. Degree in Computer Science Engineering. University of Murcia. 2007. (Level 2 (Grado) of the Spanish Higher Education Qualifications Framework (MECES) and level 6, of the European Qualifications Framework (EQF)).