Keynote Speakers

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 has resulted in more than 130 scientific papers, co-authored with over 200 collaborators from 95 universities in 31 countries. He has delivered more than 200 invited talks at conferences and scientific seminars. He was President of the International Society for Business and Industrial Statistics (ISBIS; 2013-2015), President of the Brazilian Region of the International Biometric Society (RBras; 2018-2020; 2020-2022), Council Member of the Brazilian Statistical Association (2020-2024), Co-Founder and Chairperson of the Latin American Regional Section of the International Association for Statistical Computing (2017-2019), and the Chair of the Special Interest Group on Data Science of the International Statistical Institute (2021-2023). Among other activities, he serves as the current President of the International Association for Statistical Computing, is a Member of the Representative Council of the International Biometric Society, and is a Council Member of the International Statistical Institute. Website: www.paulocanas.org; www.SaLLy.ufba.br.

Speech Title: "From Inference to Intelligence: The Evolving Role of Statistics in the Age of AI"

Abstract: Artificial Intelligence is changing the way we analyse and use data. But behind most AI methods are core ideas from statistics: uncertainty, estimation, and learning from data. In this lecture, I will show how modern AI systems, from machine learning to generative models, build upon and sometimes challenge traditional statistical thinking. We will revisit concepts such as inference, prediction, and interpretability, and see how they appear in today’s neural networks and large language models. I will also discuss why statistical reasoning is essential to make AI reliable, transparent, and fair. The goal is to open a conversation about how statistics and AI can grow together, and how statisticians can shape the future of data-driven science.