Statistical models based on stochastic partial differential equations

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Building 9, Level 2, Hall 1, Room 2322


The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

Brief Biography

David Bolin is an associate professor of statistics in the CEMSE Division at KAUST. Prior to joining KAUST, he was an associate professor in mathematical statistics at the University of Gothenburg. He received his PhD degree in mathematical statistics from Lund University in 2012 and also holds an M.S. in Engineering Mathematics from Lund University. Bolin‘s main research interests are in stochastic partial differential equations and their applications in statistics, with a focus on development of practical, computationally efficient tools for modeling non-stationary and non-Gaussian processes. He also works on uncertainty quantification and visualization for applications in spatial statistics and medical imaging. 

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