My research is at the intersection of applied, computational, and methodological statistics, motivated by the analysis of complex data. I have been mostly working on Bayesian modeling and methods, with a focus on Bayesian nonparametrics, models for latent variables, and model assessment. A leitmotif in my research is the inclusion of domain knowledge in the modeling framework. Recently, I have started working with genomic data and researching statistical methods for cancer detection. I am also interested in the development of open-source statistical software, and I am active collaborator to the nimble project, an R-based software for hierarchical models.

Sally Paganin joined the statistics faculty in 2023. She is currently an Associate Editor of the software section of The New England Journal of Statistics in Data Science. During 2021-2022, she served as Treasurer of j-ISBA, the junior section of the International Society of Bayesian Analysis (ISBA).