Menu
Top

In the Stochastic Processes & Mathematical Statistics group, we develop statistical models and methods involving stochastic processes and random fields for a wide range of applications.  

Program Affiliations

Biography

David Bolin is a professor of statistics in the CEMSE Division at KAUST, where he leads the stochastic processes and mathematical 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 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.  

Research Interests

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 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.  

Keyword tag icon
mathematical statistics stochastic processes random fields stochastic partial differential equations computational statistics

Education Profile

  • Postdoctoral Fellow, Umeå University (Sweden), 2012-2013 

  • Ph.D., Mathematical Statistics, Lund University (Sweden), 2012 

  • M.S., Engineering Mathematics, Lund University (Sweden), 2007

Awards and Recognitions

  • Best paper award, Spatial Statistics: At the Dawn of AI, 2025 

  • Invited discussion article in Statistics and Operations Research Transactions, 2024 

  • Early Investigator (ENVR) Award, American Statistical Association Section on Statistics and the Environment, 2022 

  • Elected member of the International Statistical Institute (ISI), 2020 

  • Read paper for the Royal Statistical Society, 2020 

  • Best paper award, Spatial Statistics: Emerging Patterns, 2015 

  • Cramér Prize, the Cramér section of the Swedish Statistical Society, 2013 

Publications

Research Areas

Multimedia