News and upcoming events

January 2026

• During January and February of 2026, Ashlynn Crisp (one of Lab's members) will be visiting Princeton University to work with Professor Vincent Poor Department of Electrical and Computer Engineering, as a Visiting Student Research Collaborator.

• Starting January, Jacob Schultz  (one of our Lab's members) will carry out statistical validation work for the Connectivity Assessment and Mapping for Western Burrowing Owl in New Mexico, under the direction of our collaborator Martin Lafrenz. This project continues the work we started with OCAMP (in Oregon), to identify habitat requirements, preferences, and tolerance for moving through unsuitable habitats. Identify barriers to movement, including roadways, developed areas.

December 2025

• Our paper The matryoshka doll prior – principled multiplicity correction in Bayesian model comparison (preprint on arXiv) was accepted for publication on Bayesian Analysis.  Here, we propose the matryoshka doll model space prior, which is based on the simple idea that a model’s prior probability should be proportional to the total probability of all models that nest it. This principle explicitly corrects for multiplicity and delivers a “just right” penalty on model complexity, even when the number of predictors exceeds the sample size.

November 2025

• In our paper Clustering the  Nearest Neighbor Gaussian Process with A. Crisp and A. Finley  (preprint on arxiv), we propose an approach to enhance the computational efficiency of the NNGP for Bayesian spatial regression with stationary data. Here we exploit the fact that (under the NNGP) if two different locations their corresponding distance matrices between the location and its corresponding m-nearest neighbors are the same, then their covariance will also be the same. This allows us to cluster locations to effctively reduce the computational burden while preserving the predictive ability of the NNGP.