ProfessorDavid van Dyk

David van Dyk's scholarly work focuses on methodological and computational issues involved with the Bayesian analysis of highly structured statistical models and emphasizes serious interdisciplinary research, especially in astrophysics, solar physics, and particle physics. Today’s data analysis pipelines often involve a network of research groups, where the output from one group’s analysis is an input for subsequent analyses. Van Dyk is interested in the development of principled methods for uncertainty quantification in such settings, particularly when researchers in the analysis chain use different but possibly overlapping data sets and/or employ non-congenial model assumptions. In the context of Astrostatistics, he aims to develop models and methods that capture the complexities inherent in astrophysical data, including varying detector sensitivities, observational errors, background contamination, selection effects, overlapping sources, etc. These methods must be robust to patterns of missing data and/or non-representative data. The overall models typically exhibit multilevel or hierarchical structures and combine computationally efficient data-driven or learning methods with science-driven models. The overall models are designed to leverage efficient computational techniques while enabling principled estimation and uncertainty quantification for scientifically meaningful parameters. Model selection, model checking, and sensitivity-analysis techniques are fundamental in the context of these models.

Van Dyk is particularly interested in using the complexity of the data, instruments, and models used in astrophysics to develop general-purpose statistical methods, for example to improve the efficiency of computationally intensive methods involving data augmentation, such as EM-type algorithms and various Markov chain Monte Carlo methods.