Francisco Guimarães
PhD em Enterprise Intelligence pela Universidade de Évora, PhD em Média-Arte Digital pela Universidade Aberta e Universidade do Algarve. Atualmente é docente no ISCTE, ISCAC e Universidade Europeia nos domínios de Ciência de Dados, Inteligência Artificial, Gestão de Projetos e Cibersegurança. Tem igualmente experiência como consultor em Sistemas de Informação a mais de 30 anos, com experiência no desenho e implementação de arquiteturas empresariais e de sistemas de informação em países como Portugal, Angola, Cabo Verde, Moçambique e Emirados Árabes Unidos, em setores de atividade de Banca, Seguros, Indústria, Utilities, Telco e Setor Público, incluindo com reguladores de mercados financeiros.
PhD em Enterprise Intelligence pela Universidade de Évora
PhD em Média-Arte Digital pela Universidade Aberta e Universidade do Algarve
Professor Auxiliar, Universidade Europeia
Likhi Rubio
Lihki Rubio is an Associate Professor in the Department of Mathematics and Statistics at Universidad del Norte (Colombia), where he serves as Coordinator of the Master's program in Applied Statistics. He holds a PhD in Mathematical Engineering and has a strong interdisciplinary background in scientific computing, machine learning, and statistical modeling. His research focuses on advanced predictive analytics, particularly time series forecasting through hybrid approaches that integrate statistical methods with deep learning architectures. He has authored multiple publications in high-impact journals such as Neural Computing and Applications, Energy, and Eurasian Economic Review, contributing to areas including financial analytics, energy forecasting, and applied artificial intelligence. Dr. Rubio combines academic leadership with industry experience, having previously worked as Chief Technology Officer in blockchain-based financial systems and led applied machine learning initiatives for business decision-making. His recent work, “Optimizing wind power forecasting with hybrid neural networks: insights from Brazil”, highlights the role of AI-driven models in improving renewable energy forecasting and supporting sustainable energy systems. His broader interests include Business Analytics, Artificial Intelligence, and data-driven decision-making, aligning closely with the themes of BizData 2026.
PhD in Mathematical Engineering
Assistant Professor at Universidad del Norte, Colombia
Submission period: 6 March – 13 April 2026. Full papers (max 15 pages) and extended abstracts (1–2 pages) accepted. Free registration for accepted presenters.
Submit via Microsoft CMT