Dr. Datta is an Associate Professor in the Department of Biostatistics at Johns Hopkins University. He completed his PhD. in Biostatistics from University of Minnesota. Dr. Datta’s research focuses on developing spatial models for geographically indexed data. His work on Nearest Neighbor Gaussian Processes (NNGP) has become one of the most widely used methods for scalable analysis of massive geospatial data. His recent work focuses on developing theory and methodology for combining machine learning algorithms with traditional spatial modeling, and application of the methodology to air pollution and infectious disease modeling. He also works on developing Bayesian hierarchical models for multi-source data with applications in global health.

Renato Martins Assunção is a researcher with dual affiliations: he is a Professor in the Department of Computer Science at the Universidade Federal de Minas Gerais (UFMG), Brazil, and a researcher in the Spatial Statistics Team at Esri Inc., USA. He holds a Ph.D. in Statistics from the University of Washington. Professor Assunção’s research focuses on spatial statistics, machine learning, and Bayesian statistics, emphasizing the development of innovative methodologies for spatial data analysis. With publications in top-tier journals and conferences, he has made contributions mainly to computational spatial statistics.

Lola Ugarte is a full professor at the Public University of Navarre, where she heads the Spatial Statistics research group. Her primary research interests focus on developing methodologies in spatial and spatio-temporal statistics, with applications in diverse fields such as epidemiology, violence against women, and remote sensing. In 2021, she was honored with the SEIO-BBVA Award for the best contribution to Applied Statistics. In June 2024, she received the Spanish Society of Statistics and O.R. (SEIO) Medal. Currently, she serves as the director of the INAMAT2 Research Institute (Institute for Advanced Materials and Mathematics) at UPNA and as the president of FENStatS (The Federation of European National Statistical Societies).

Murali Haran is Professor of Statistics at Penn State University. He has a PhD in Statistics from the University of Minnesota, and a BS in Computer Science (with minors in Statistics, Mathematics and Film Studies) from Carnegie Mellon University. His research interests are in the statistical analysis of complex computer models, models for spatial data, and Monte Carlo algorithms, particularly for inference with intractable likelihoods. Much of his research is interdisciplinary, motivated by statistical challenges in climate science and infectious disease. He received the 2015 Young Researcher Award from The International Environmetrics Society (TIES) to “recognize and honor outstanding contributions to the field of environmetrics”.

Alessandro Fassò is a full professor of Statistics at the Department of Economics, University of Bergamo, Italy. He is Editor in Chief of Environmetrics and author of more than hundred papers, mainly on statisticalmethods and applications to environmetrics, air quality, climate variables, sensitivity analysis ofenvironmental models, environmental time-series, spatio-temporal data, etc. Since 2021, he is working in the connection of Intensive farming and air quality. In particular he has been working on spatio-temporal statistical models applied to the impact analysis of intensive farmingof bovine and swine on air quality. In particular, he is contributing to the understanding of therelationship between ammonia and fine particulate matter in the North of Italy.

Renato Martins Assunção is a researcher with dual affiliations: he is a Professor in the Department of Computer Science at the Universidade Federal de Minas Gerais (UFMG), Brazil, and a researcher in the Spatial Statistics Team at Esri Inc., USA. He holds a Ph.D. in Statistics from the University of Washington. Professor Assunção’s research focuses on spatial statistics, machine learning, and Bayesian statistics, emphasizing the development of innovative methodologies for spatial data analysis. With publications in top-tier journals and conferences, he has made contributions mainly to computational spatial statistics.

Ronny O. Vallejos earned his Bachelor’s and Master’s degrees in Mathematics from the Universidad Técnica Federico Santa María, Chile, in 1995 and 1998, respectively. He also obtained a Master’s degree in Statistics from the University of Connecticut, USA, in 2002, followed by a Ph.D. in Statistics from the University of Maryland, Baltimore County, in 2006. He is currently an Associate Professor in the Department of Mathematics at the Universidad Técnica Federico Santa María, Chile. Additionally, he serves as an Associate Editor for the Journal of Agricultural, Biological, and Environmental Statistics and the Brazilian Journal of Probability and Statistics. His research interests include spatial statistics, statistical image processing, time series analysis, and agreement measures.

Juan Sosa is a professional in Statistics with a master's degree in Statistics from the National University of Colombia and a Ph.D. in Statistics and Applied Mathematics from the University of California, Santa Cruz. He is an Associate Professor in the Department of Statistics at the National University of Colombia, where he serves as researcher and research coordinator. Currently, he develops methodologies to address problems involving complex data structures in the Economic, Political, Social, and Health Sciences. Specifically, his research focuses on social network analysis, parliamentary voting analysis, longitudinal data analysis, and record linkage.

Francisco Cuevas has received his Mathematical Engineering degree and the master's degree in mathematics from the Universidad Técnica Federico Santa María, Chile, in 2013, and his Ph.D. degree in Mathematics from Aalborg University, Aalborg, Denmark, in 2019. Between 2017 and 2019, he worked in the Centre for Stochastic Geometry and Advanced Bioimaging (CSGB), focusing his research in random fields and spatial point patterns.During 2019 he was working as a postdoc in Université du Québec à Montréal (UQAM), and during 2020 as a postdoc in the Advanced Center for Electrical and Electronic Engineering (AC3E). He is currently an academic in the Department of Mathematics at Universidad Técnica Federico Santa María (UTFSM). His research interests encompass point patterns, random fields, and functional data analysis.

Dr Jonatan A. González is an Assistant Professor in the Department of Statistics and Operations Research at the Universidad Miguel Hernández de Elche (Spain). His research sits at the intersection of spatial statistics and computational methodologies, with a strong emphasis on point process modelling, epidemiology, infectious disease modelling, and the development of application-focused methods.Dr González holds a bachelor's degree and a master's in mathematics, and he completed his PhD at the Universitat Jaume I, specialising in spatial and spatio-temporal point processes. His work has addressed a range of topics, including replicated point pattern observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design, and local spatial characteristics.