Prof.Paulo Canas Rodrigues Federal University of Bahia, Brazil

Paulo Canas Rodrigues is a Professor of Statistics and Data Science at the Federal University of Bahia and the Director of the Statistical Learning Laboratory (SaLLy; www.SaLLy.ufba.br). Paulo completed his Ph.D. in Statistics at the Nova University of Lisbon, Portugal (2012), and his Habilitation in Mathematics, with a specialization in Statistics and Stochastic Processes, at the Lisbon University, Portugal (2019). His research in time series forecasting, statistical learning, artificial intelligence, statistics, and data science resulted in more than 130 scientific papers in collaboration with more than 200 co-authors from 95 universities in 31 countries and delivered more than 200 invited talks at conferences and scientific seminars. He is an Elected Member of the International Statistical Institute. Among other activities, he is the current President of the International Association for Statistical Computing, the Past-President of the International Society for Business and Industrial Statistics, a Member of the Representative Council of the International Biometric Society, and a Council Member of the International Statistical Institute. Website: www.paulocanas.org; www.SaLLy.ufba.br.

Title: From Data to decisions: Statistics, AI, and real-world business applications

Abstract: In recent years, data science and artificial intelligence have become central to decision-making in business and industry. However, the real value of these tools does not come from complexity alone, but from how different approaches are combined and applied to real problems. In this talk, I present a practical perspective on data-driven decision-making, bringing together statistical models, machine learning, and more recent AI approaches. Rather than focusing on a single method, I show how these techniques can be used in a complementary way, with each model capturing different aspects of the data. Examples will include forecasting, pattern discovery, and decision support in real-world settings, with applications ranging from environmental monitoring to public policy and business contexts. I will also briefly discuss how recent developments, such as large language models, are changing the way we interact with data and support decisions.