https://stan4pmx2026.sciencesconf.org/Workshops are contributor-led mini-conferences that explore focused topics in Stan, probabilistic programming, or related methods. Organisers curate their own program of talks, discussions, and activities. This new format empowers the community to shape the meeting by diving deep into emerging areas and shared interests. Below are the accepted workshops at Stancon 2026.

Presentation of confirmed workshops

Stan for pharmacometrics

Short description: This symposium will showcase recent advances and practical experiences at the interface between Stan and Bayesian modeling of longitudinal biomarkers data, with a focus on pharmacokinetics, pharmacodynamics, and disease progression. Building on a series of dedicated Stan-for-pharmacometrics meetings, the session will highlight how Stan enables flexible model specification, rigorous uncertainty quantification, and modern workflow integration for complex longitudinal and hierarchical data.

Link: https://stan4pmx2026.sciencesconf.org/ 

Organizers: Julie Bertrand and Nathanael Hoze, Inserm, FR.  https://www.inserm.fr/

Bayesian Inference for Sparsity-Promoting and Edge-Preserving Priors in Probabilistic Programming

Short description: 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.

Link: https://www.janglaubitz.com/stancon2026