WELCOME TO THE AUSTRALASIAN MATHEMATICAL PSYCHOLOGY CONFERENCE 2026
23 Feb – 25 Feb 2026
Shaw Foundation Alumni House, National University of Singapore
AMPC 2026
The Australasian Mathematical Psychology Conference (AMPC) convenes in Singapore from
February 23-25, 2026
marking the first time the event will be hosted in Asia. The conference aims to expand global participation in mathematical and quantitative psychology.
The Theme of the 2026 Conference
“Applying Mathematical and Quantitative Psychology to Real World Complex Problems“
Subthemes
Applied Mathematical Psychology in Health and Performance
Artificial Intelligence, Big Data, and Human Behavior
Computational and Cognitive Modelling in Psychology
Advances in Mathematical Psychology and Psychometrics
Keynote Speakers
Dr. Roger Ratcliff is a distinguished professor in the Department of Psychology at The Ohio State University. He is renowned for developing the diffusion model, a theory of simple decision-making that has become a standard in the field. This model explains key aspects of experimental data, including accuracy and response times.
Dr. Ratcliff’s work is widely applied in fields such as aging, neuroscience, and clinical applications. He was elected to the American Academy of Arts and Sciences in 2015 and has received numerous awards, including the Warren Medal from the Society of Experimental Psychologists and the Troland Research Award from the National Academy of Sciences.
Dr. Irini Moustaki is a Professor of Social Statistics at The London School of Economics and a Fellow of the British Academy. Her research focuses on latent variable models, structural equation modelling, and psychometrics, with applications in education, health, and social policy.
She has made significant contributions to the analysis of categorical data, handling missing data, and modelling heterogeneous populations. Her work bridges statistical theory and practical research, including cross-national studies and epidemiological investigations. Her methodological innovations have advanced the robustness and interpretability of social science data.
Co-Organized by
