SPSTAt 2026
International Conference on Statistics, Data Science, and Analytics
Speakers
Meet the researchers and practitioners sharing their work at the conference.
Plenary Speakers
Prof. Dr. Olivier Thas
Hasselt University, Belgium · Professor, Ghent University, Belgium · Honorary Professor, University of Wollongong, Australia
His research focuses on the development and application of statistical methods for challenges in the biosciences, with particular expertise in the analysis of high-dimensional, high-throughput data, including microbiome and genomics research.
Prof. Dr. Thomas Neyens
UHasselt Data Science Institute (DSI) & Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat) · Professor of Biostatistics, KU Leuven, Belgium
He is the Flemish promoter of the Co-CiPhil ITP Project, where he leads and supports research on quantitative analysis through citizen science and spatio-temporal analysis of ecological and epidemiological data. His contributions to the project include delivering workshop courses, mentoring research initiatives, and facilitating the development of citizen-science studies and stakeholder engagement activities.
Prof. Dr. Paulo Canas Rodrigues
Federal University of Bahia (UFBA), Brazil · Director, Statistical Learning Laboratory (SaLLy)
His research focuses on statistical learning, artificial intelligence, time series forecasting, and high-dimensional data analysis. He currently serves as President of the International Association for Statistical Computing (IASC), and is Past President of the International Society for Business and Industrial Statistics (ISBIS), a Council Member of the International Statistical Institute (ISI), and a Representative Council Member of the International Biometric Society (IBS). He has delivered lectures, workshops, and keynote presentations across Africa, Asia, Europe, Oceania, and the Americas.
Prof. Dr. Daniel Andrade
Education and Research Center for Artificial Intelligence and Data Innovation, Hiroshima University
His research covers a broad field in statistics and applied machine learning, including natural language processing (NLP) and uncertainty quantification. His current research focuses especially on Bayesian inference methodology and medical data analysis using large language models. Prof. Daniel received his diploma in computer science from the University of Passau in Germany in 2007, his PhD in computer science from the University of Tokyo in 2011, and his PhD in statistics from the Graduate University for Advanced Studies (SOKENDAI) with the Institute of Statistical Mathematics in 2019. Before joining Hiroshima University in 2021, he worked as a researcher at NEC Central Research Laboratories, Japan. Prof. Daniel has numerous publications in leading international machine learning conferences and journals. He is also a recipient of several awards, including the Special Industrial Achievement Award from the Information Processing Society of Japan, and a best paper award for his work on the Dempster-Shafer theory from IEEE-SMCia.