Joel Eliason

Joel Eliason is a postdoctoral researcher in the Popel Lab at Johns Hopkins University, specializing in computational biology and spatial statistics. His work focuses on developing Bayesian methods for modeling asymmetric spatial associations between cell types in tissue microenvironments.

• Developed the SHADE R package for directional spatial analysis
• Co-created DIMPLE framework for cell-cell interaction modeling

Research interests span:

• Multilevel Bayesian modeling
• Uncertainty quantification in virtual clinical trials
• Spatial point process modeling
• Tumor microenvironment analysis

Recent publications demonstrate expertise in:

• High-dimensional multiplex imaging analysis
• Spatial interaction curves (SICs)
• Joint modeling across subjects
• Tensor decomposition for spatial contexts

Technical skills include:

• Stan and CmdStanr for Bayesian inference
• Reproducible R package development
• Quarto and knitr for computational documentation