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
- Scalar-on-image regression via the soft-thresholded Gaussian process (2018)
- Characterizing sources of uncertainty from global climate models and downscaling techniques (2017)
- Optimal seed deployment under climate change using spatial models: Application to loblolly pine in the Southeastern US (2017)
- A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints (2017)
- A spatial model for rare binary events (2017)
- A multivariate dynamic spatial factor model for speciated pollutants and adverse birth outcomes (2017)
- A space-time skew-t model for threshold exceedances (2017)
- Using spatial cross-correlation image analysis to characterize the influence of strain rate on plastic damage in molecular dynamics simulations (2017)
- Spatial prediction of crystalline defects observed in molecular dynamic simulations of plastic damage (2017)
- Spatial prediction of crystalline defects observed in molecular dynamic simulations of plastic damage (2017)