Brian uses geospatial analytics to explore patterns in data from the environmental, physical and materials sciences. He develops approaches using spatial statistics, extreme value analysis, quantile regression, variable selection and dimension reduction. His interests include both methodological questions in statistics and applications of statistical methods to problems such as air pollution and climate change.
Roles
Publications
- A BAYESIAN SEMI-PARAMETRIC MIXTURE MODEL FOR BIVARIATE EXTREME VALUE ANALYSIS WITH APPLICATION TO PRECIPITATION FORECASTING (2021)
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications (2021)
- A spatial Bayesian semiparametric mixture model for positive definite matrices with applications in diffusion tensor imaging (2021)
- Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials (2021)
- Bayesian variable selection for high-dimensional rank data (2021)
- Deep distribution regression (2021)
- Flexible and Fast Spatial Return Level Estimation Via a Spatially Fused Penalty (2021)
- Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging (2021)
- Landscape-level variation in Bt crops predict Helicoverpa zea (Lepidoptera: Noctuidae) resistance in cotton agroecosystems (2021)
- Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model (2021)