Profiles

David Bolin is a professor of statistics in the CEMSE Division at KAUST, where he leads the Stochastic Processes and Mathematical Statistics (StochProc) research group. Before joining KAUST, he was an associate professor of mathematical statistics at the University of Gothenburg. He received his Ph.D. in mathematical statistics from Lund University, Sweden, in 2012. 

Bolin's research focuses on stochastic partial differential equations (SPDEs) and their applications in statistics, with an emphasis on developing practical, computationally efficient tools for modeling non-stationary and non-Gaussian processes. He has made significant contributions to the theory of Gaussian processes, optimal linear prediction, fractional-order SPDEs, and stochastic processes on metric graphs. He has also developed and maintains several widely used software packages for advanced statistical modeling. 

Bolin is an elected member of the International Statistical Institute, and has received multiple honors, including the Section on Statistics and the Environment Early Investigator Award from the American Statistical Association and the Cramér Prize from the Cramér section of the Swedish Statistical Society. 

Professor Bolin’s main research interests are stochastic partial differential equations (PDEs) and their applications in statistics, focusing on developing practical, computationally efficient tools for modeling non-stationary and non-Gaussian processes. 

He leads the stochastic processes and mathematical statistics research group (StochProc) at KAUST, which focuses on statistical methodology for stochastic processes and random fields based on stochastic PDEs. 

His research combines methods from statistics, probability, and applied mathematics to construct more flexible statistical models and develop better computational methods for statistical inference. In parallel with the theoretical research, the group works on applications in a wide range of areas, ranging from brain imaging to environmental sciences.  

Alexandre Simas is a senior research scientist at KAUST in the Stochastic Processes and Mathematical Statistics group. His work spans probability, statistics and mathematical analysis, with a particular focus on stochastic partial differential equations, random fields on complex domains and statistical methodology for spatial and graph-based data.

He received his Ph.D. in Mathematics from the Instituto Nacional de Matemática Pura e Aplicada (IMPA) in Brazil, and part of his doctoral research was conducted at the Courant Institute of Mathematical Sciences at New York University under the mentorship of S. R. S. Varadhan. Before joining KAUST, Alexandre was a faculty member at the Federal University of Paraíba, where he reached the position of associate professor.

His research includes contributions to the theory and computation of SPDE-based models, Gaussian and non-Gaussian processes, fractional and non-stationary fields, and statistical modeling on metric graphs and manifolds. He is also active in the development of open-source statistical software, co-authoring the MetricGraph, rSPDE and ngme2 packages, among others.

Alexandre’s research focuses on stochastic partial differential equations, probability theory and spatial statistics, with particular emphasis on the mathematical and statistical modeling of Gaussian and non-Gaussian random fields on metric graphs and other complex geometries. His work develops theoretical foundations for SPDE-based models on graph-structured domains, including results on covariance structures, regularity properties and computationally efficient approximation methods.

He also works on Bayesian inference for large-scale SPDE models, fractional and non-stationary fields, and on the construction of flexible statistical frameworks for data defined on networks and irregular spatial domains. In parallel with these theoretical developments, Alexandre contributes to the creation of open-source R software, including the MetricGraph package, the rSPDE package and the ngme2 package, which provide tools for modeling and inference in applications ranging from environmental processes to network-based phenomena.

Andrea Rocha is a research specialist in statistics at KAUST, working with the Stochastic Processes and Mathematical Statistics group. Before joining KAUST, she was an associate professor in the Department of Scientific Computing at the Federal University of Paraíba (UFPB), Brazil, where she also held various academic and coordination roles over nearly 15 years.

Her academic background includes a Ph.D. in Computational Mathematics, an M.Sc. in Statistics, and a B.Sc. in Statistics, all from the Federal University of Pernambuco (UFPE). Her doctoral and master’s work was supervised by Professor Andrei Toom.

Andrea’s research bridges theory and application in computational statistics, with a focus on probabilistic modeling, regression analysis, and stochastic processes. She has authored several publications in international journals and co-authored academic books on probability and random processes.

Andrea’s research lies at the intersection of computational mathematics, statistical inference, and applied probability. Her main areas of interest include:

• Statistical modeling of complex data using Bayesian methods
• Machine learning techniques for regression and classification
• Probabilistic simulation and stochastic processes
• Diagnostic and influence analysis in regression models
• Dispersion models and beta regression

She has contributed to the theory and application of statistical methods through both individual research and collaborations.

I am a PostDoc at King Abdullah University of Science and Technology (KAUST) in Stochastic Processes and Mathematical Statistics Research Group.

I am interested in mathematical statistics, Markov chains, Monte Carlo methods, and stochastic analysis. My research is focused on theoretical and applied problems associated with stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs). The goal is to propose and establish methods that are well-studied theoretically and provide numerical implementations that corroborate the proven theoretical findings and their efficacy.

Markov Chain Monte Carlo, Particle Methods, Stochastic Control, Machine Learning, Stochastic Partial Differential Equations

João Vitor Cordeiro de Brito is a MS/PhD student in the Statistics Program at King Abdullah University of Science and Technology (KAUST).

Prior to KAUST, he obtained his degree in Applied Mathematics from the Federal University of Rio de Janeiro (UFRJ), Brazil. During his bachelors, João did undergraduate research on ergodic aspects of problems from number theory, and was awarded with honors/prizes in national and international contests in mathematics.

His research interests lie primarily in the intersection of harmonic analysis, statistical inference, and network science, with applications in neuroscience, among others.