This workshop will highlight emerging methods for computationally efficient Bayesian inference in inverse problems and related high-dimensional models with sparsity-promoting and edge-preserving priors. Such priors often induce heavy-tailed, multimodal posteriors that challenge standard sampling and optimization, motivating new scalable strategies including hierarchical and mixture prior constructions, diffusion- and transport-based sampling, and advanced approaches to uncertainty quantification. A central aim is to connect these methodological developments to practical implementation in modern probabilistic programming frameworks—particularly Turing/Julia and, more broadly, Stan—emphasizing algorithmic advances that enable efficient inference at scale. The program will consist of curated invited talks followed by discussion sessions designed to foster exchange between method developers and users, identify key opportunities for probabilistic programming practitioners, and catalyze cross-community collaboration spanning inverse problems, Bayesian computation, numerical analysis, data assimilation, and scientific machine learning.

TBA

The workshop will take place on Friday, August 21, 2026

Uppsala, Sweden

• Jan Glaubitz (Assistant Professor, Department of Mathematics, Linköping University, Sweden)

• Yiqiu Dong (Associate Professor, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark)

• Lassi Roininen (Professor, School of Engineering Sciences, LUT University, Finland)