António Filipe Fonseca is an engineer and complexity sciences researcher. He graduated in Electronics and Telecommunications from IST in Lisbon and holds a Master's degree and a PhD in Complex Systems Sciences from ISCTE/IUL and Faculdade de Ciências de Lisboa. His main research interests are complex social systems, information dynamics and modelling and simulation of complex systems. He worked in several private companies as an engineer before joining the public service as an IT expert. Currentlys is Assistant professor in ISCTE Sintra and Integrated Researcher  at ISTAR/ISCTE -IUL Lisbon.

António Raimundo, who holds a PhD in Information Science and Technology from Iscte - University Institute of Lisbon, Portugal, is an Assistant Professor at the School of Applied Digital Technologies at Iscte Sintra, where he also coordinates the Bachelor's degree in Digital Technologies and Artificial Intelligence. Currently, he is an Integrated Researcher at ISTAR-IUL, focusing his research on areas such as Machine Learning / Deep Learning, Large Language Models, Computer Vision, and Data Science. With several years of teaching experience, he has had the opportunity to be a trainer in short courses, postgraduate programs, and a speaker at various business events.

Patrícia Costa holds a PhD in Production and Systems Engineering (thesis in the area of statistics/psychometrics) from the University of Minho, a Master's degree in Mathematics - Teaching (thesis in the area of statistics/psychometrics) from the University of Beira Interior and a degree in Mathematics - Educational Training from the University of Coimbra. Her research focuses on Educational Assessment, Applied Statistics, Comparative Education and Psychometrics. She has experience in teaching in Higher Education, research and providing technical support to policy makers at the Joint Research Center (JRC) of the European Commission. She is the author/co-author of several scientific articles in international journals, book chapters and technical reports and has experience in organising international scientific events.

Paulo Vieira is an invited professor at ISCTE-IUL and student researcher at LASIGE Computer Science and Engineering Research Centre. His academic background includes a 1st cycle degree in Computer Engineering from Instituto Superior de Engenharia de Lisboa, a Master's degree in Computer Engineering from Instituto Superior de Engenharia de Lisboa. He is currently working on his PhD in Informatics from Faculdade de Ciências da Universidade de Lisboa. His research interests focus on emotion, machine learning and information systems.  

Has a bachelor degree in Informatics Engineering in 1997 by the Faculty of Sciences and Technology of the New University of Lisbon.  He obtained a master degree in Applied Artificial Intelligence in 200 by the Faculty of Sciences and Technology of the New University of Lisbon under the supervision of Luís Correia.  He obtained a doctoral degree in Informatics in 2006 by the Faculty of Sciences of the University of Lisbon under the supervision of Luís Correia. He has taught in the Electronics, Telecommunications and Informatics Department of the University of Aveiro and in the Informatics Department of the Faculty of Sciences of the University of Lisbon. Among the courses he taught are Operating Systems, Artificial Intelligence, Programming, Automata Theory, Data Bases, and Distributed Systems. He has participated in the European projects ASSISIbf and InfoHabitants. He was a co-principal investigator in projects funded by the research unit BioISI. He is the co-principal investigator in the INTERPHENO project funded by the Portuguese science foundation. He is a researcher in the ExpoLIS projected funded by FEDER. He is a collaborator of the research units BioISI and Lasige. He was a program committee member in several conferences from the Artificial Life field. He has participated in the organisation of Portuguese conferences as well as Portuguese programming competitions. His research interests are focused in applying techniques from machine learning to the generic game playing field and to the classification of real world data.