Keynote Speakers

Dr. Ding-Geng Chen (aka Din Chen) is an elected fellow of the American Statistical Association (FASA), an elected Fellow of the Royal Society of South Africa (FRSSAf), and elected Member of the Academy of Science of South Africa. He is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. He is also an extraordinary professor and the SARChI research chair in biostatistics at the University of Pretoria, an honorary professor at the University of KwaZulu-Natal, South Africa. Dr. Chen was the Wallace H. Kuralt distinguished professor in Biostatistics at the University of North Carolina at Chapel Hill, a professor in biostatistics at the University of Rochester Medical School, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University. Dr. Chen has more than 300 referred professional publications and co-authored and co-edited 45 books on biostatistics, clinical trial methodology, meta-analysis, data science, causal inference, and public health research.

Speech Title: "Biostatistics and ML/AI in High-Dimensional Medical Imaging Analysis"

Abstract: Non-mydriatic retinal fundus imaging is widely used for screening diabetes, glaucoma, cataracts, cardiovascular diseases, and neurodegenerative disorders. However, these images often suffer from noise, low contrast, and structural distortions, which limit the performance of artificial intelligence and machine learning models that rely on accurate visual features. In this keynote, we present the development of biostatistical framework of robust PCA with ML/AI that addresses existing limitations in retinal image enhancement by integrating a Truncated Weighted Nuclear Norm (TWNN) with Adaptive Histogram Equalization (AHE). Multiple synthetic and publicly available retinal datasets, including EyeQ, DRIVE, STARE, and Kaggle, will be used to demonstrate that the proposed method substantially improves image fidelity and clearly reveals fine blood vessels. A user-friendly APP is created to enable rapid enhancement of degraded retinal images for clinical use. This framework establishes a robust methodological foundation for next-generation high-dimensional medical imaging analytics and accurate ML/AI based disease detection, bringing us closer to real-time, clinically actionable retinal image analysis.

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.