Profiles

David Bolin is an associate professor of statistics in the CEMSE Division at KAUST, where he leads the Stochastic Processes and Applied Statistics 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 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 serves as an associate editor for the Scandinavian Journal of Statistics, 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.

The Swedish researcher leads the Stochastic Processes and Applied Statistics (StochProc) research group at KAUST, which focuses on statistical methodology for stochastic processes and random fields based on stochastic PDEs.

This research combines methods from statistics, probability and applied mathematics in order to construct more flexible statistical models and 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 research scientist at KAUST in the Stochastic Processes and Applied Statistics group. His research lies at the interface of probability, statistics, and mathematical analysis, with a strong focus on stochastic partial differential equations (SPDEs), random fields on complex domains, and statistical computation.

He earned his Ph.D. in Mathematics from the Instituto Nacional de Matemática Pura e Aplicada (IMPA), Brazil, with part of his doctoral research conducted at the Courant Institute of Mathematical Sciences, NYU, under the mentorship of S. R. S. Varadhan. Before joining KAUST, Alexandre held faculty positions at the Federal University of Paraíba (UFPB), where he was an associate professor in the Department of Mathematics.

He has co-developed several R packages, including rSPDE, MetricGraph, and ngme2, and has a strong publication record in areas such as spatial modeling, beta regression, Gaussian processes, and functional Itô calculus.

Alexandre’s research focuses on:

• Gaussian random fields on metric graphs and manifolds
• Bayesian inference for SPDE-based models
• Functional analysis and partial differential equations
• Interacting particle systems and hydrodynamic limits
• Statistical computing and the development of open-source tools in R

He actively collaborates on both theoretical and computational developments for modeling complex data with underlying spatial or graph-based structures.

Andrea Rocha is a research specialist in statistics at KAUST, working with the Stochastic Processes and Applied 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 Applied 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.