Focus Period Halmstad University 2026
Professor
Federal University of Santa Maria (Brazil)
Prof. Luís A. de Lima Silva holds B.Sc. and M.Sc. degrees in Computer Science from the Federal University of Rio Grande do Sul (UFRGS) and a Ph.D. in Computer Science from University College London (UCL, UK). He is affiliated with the Department of Applied Computing at the Federal University of Santa Maria (UFSM), where he teaches in the Computer Science, Information Systems, and Computer Engineering programs, and is a permanent member of the Graduate Program in Computer Science (PPGCC/UFSM). His research focuses on the development and application of Artificial Intelligence (AI) methods, including Deep Learning, Generative Adversarial Networks (GANs), Transfer Learning, Explainable AI (XAI), Clustering, Reinforcement Learning, and Case-Based Reasoning. His current research spans Cybersecurity, Defense, Health, Environment, and Computer Games. In Defense, he has participated in the SIS-ASTROS projects since 2004, a partnership between UFSM and the Brazilian Army focused on simulation systems for the tactical training of ASTROS artillery batteries. His research includes agent-based simulation, path planning in large-scale virtual environments, and AI-based decision support. He currently coordinates a FAPERGS-funded project on Transfer Learning, Transformers, and GAN architectures for synthetic data generation in Digital Health and Cybersecurity. He is also a member of the CIARS network project on AI for healthcare and a tutor in the PET Saúde Digital UFSM program, an initiative of the Brazilian Ministry of Health. His research also addresses Deep Learning models for climate resilience of river basins and road ecology, as well as algorithms for competitive and collaborative multi-agent games involving imperfect information and bluffing, particularly the card game Truco. Previously, he participated in the Petrographer Project on Knowledge Engineering for petroleum reservoir assessment and conducted research on AI for the authentication and dating of historical paintings.