Helio S. Migon graduated in Statistics from Escola Nacional de Ciências Estatísticas in 1970, obtained his MSc in Statistics from the University of São Paulo in 1974, and completed his PhD in Statistics at the University of Warwick in 1984. He is Professor Emeritus at the Federal University of Rio de Janeiro and has been one of the pioneers of Bayesian dynamic modelling and Bayesian forecasting in Brazil. His research contributions cover Bayesian inference, dynamic models, Bayesian forecasting, finite population sampling, econometrics, finance, actuarial science, hierarchical models and Bayesian computation. He has been a CNPq research fellow since 1989.

His scientific production includes articles in leading journals such as the Journal of the American Statistical Association, the Journal of the Royal Statistical Society Series B and Biometrika. He is also co-author, with Dani Gamerman and Francisco Louzada, of Statistical Inference: An Integrated Approach, published by Chapman & Hall/CRC. Over his career, he has supervised a remarkable number of graduate students, including 29 doctoral theses and 44 master's dissertations, playing a central role in the training of researchers who now contribute to Statistics in Brazil and abroad.

Helio Migon has also made major institutional contributions. He served as President of the Brazilian Statistical Association, as a member of the Advisory Committee for Mathematics, Probability and Statistics at CNPq, as Adjunct Coordinator of the area of Mathematics, Probability and Statistics of CAPES, as Coordinator of the Graduate Program in Statistics at UFRJ, as Deputy Director for Research at the Institute of Mathematics–UFRJ, and as Head of the Department of Statistical Methods–UFRJ.

Dani Gamerman graduated in Mechanical Engineering from Instituto Militar de Engenharia in 1980, obtained his MSc in Statistics from IMPA in 1983, and completed his PhD in Statistics at the University of Warwick in 1987. He was Full Professor at UFRJ from 1996 to 2019 and became Professor Emeritus of UFRJ in 2021. He was also Visiting Full Professor at UFMG from 2019 to 2021 and has been a CNPq research fellow since 1987.

Dani Gamerman is internationally recognised for his contributions to Bayesian inference, Markov chain Monte Carlo methods, stochastic simulation, dynamic modelling, spatial statistics, survival analysis, extreme value theory and item response theory. His book Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, first published by Chapman & Hall in 1997 and later expanded in collaboration with Hedibert F. Lopes, became an important reference for Bayesian computation. He is also co-author of Statistical Inference: An Integrated Approach and Building a Platform for Data-Driven Pandemic Prediction: From Data Modelling to Visualisation — The CovidLP Project, reflecting his broad engagement with both methodological and applied statistical research.

His articles have appeared in major journals such as the Journal of the Royal Statistical Society, Series B, Biometrika, Science, Bayesian Analysis and Journal of the Royal Statistical Society Series C, among others. He has been an invited speaker at several major international meetings, including the Valencia Meetings, the World Meeting of the International Society for Bayesian Analysis, and the International Workshop on Statistical Modelling. He has also served as associate editor of several journals and has been a member of the ISBA Board, the ABE Board and the Advisory Board of Bayesian Analysis. Public records list 21 doctoral theses and 20 master's dissertations completed under his supervision.