Local organizing committee of StanCon 2026

Måns Magnusson (Uppsala University, Sweden)

Associate Professor Magnusson is a lecturer and researcher in statistics at Uppsala University. Magnusson focus on scalable inference, probabilistic machine learning and applied modeling. His contributions span Bayesian inference, data-efficient machine learning, and the development of computational methods for inference from textual data. He is a Stan developer, bridging methodological innovation with practical applications in the applied research problems.

Sara Hamis (Uppsala University, Sweden)

Dr Hamis is an assistant professor in machine learning at Uppsala University. Her work lies at the interface of applied mathematics and probabilistic machine learning, with a focus on modelling biological systems. She develops methods for uncertainty quantification and robust inference, and contributes to interdisciplinary collaborations in the health and life sciences aimed at predicting and understanding complex biological mechanisms.

More information at uu.se

Program committee of StanCon 2026

Aki Vehtari (Chair, Aalto University, Finland)

Professor Vehtari is a leader in computational Bayesian modelling at Aalto University (Finland), whose work spans probabilistic programming, Bayesian workflow methods, model diagnostics and hierarchical models. His contributions include promoting open course materials and advancing inference techniques for modern Bayesian workflows. 

Mitzi Morris (Columbia University, USA)

Mitzi Morris is an independent consultant and long-time member of the core development team for the Stan probabilistic programming language, working on the C++ core and Python interface (CmdStanPy). In addition to Stan’s computing infrastructure, she contributes to documentation, case studies on spatial epidemiology, and collaborations with epidemiologists.

Mattias Villani (Stockholm University, Sweden)

Professor Villani holds the Chair of Statistics at Stockholm University since 2018, and previously worked at the central bank of Sweden and at Linköping University. His research focuses on computationally efficient Bayesian methods for inference, prediction and decision-making in flexible probabilistic models.

Vianey Leos Barajas (University of Toronto, Canada)

Assistant Professor Leos Barajas is cross-appointed between the Department of Statistical Sciences and the School of the Environment at University of Toronto. Her research focuses on Bayesian statistics, time-series and Markov-switching models, with applications in statistical ecology and environmental data science.

Mariel M. Finucane (Blue Cross Blue Shield of Massachusetts, USA)

Dr Finucane has played influential roles in global health and statistical methods. Her work includes extensive contributions to large-scale Bayesian modelling of health outcomes, such as trends in body-mass index globally.