Active Projects
Translating peer-reviewed quantitative models into custom computational workflows for forest genetics, crop management, and systems biology.
Precision Genomic Offset Estimation
Implementing a novel deep learning architecture to estimate and map the genomic vulnerability of forest species. Rather than calculating average population offsets, our framework models climate mismatch at the level of the individual genotype, allowing us to predict precise adaptive limits to guide targeted assisted migration.
- Methodology: Deep Learning, Climate Projection Ensembles, Genomic Offset Modeling
- Focus: Climate adaptation mapping & assisted migration
Reference Genome Assembly for Populus tremuloides
Assembling a high-quality reference genome for trembling aspen. Integrating long-read sequencing technologies, transcriptomics, and population-level genetic analyses to characterize clonal structure and adaptive variations across regional ranges.
- Methodology: Long-Read de novo Assembly, RNA-Seq Transcriptomics, Population Structure
- Focus: Clonal forest genetics & breeding selection
Genomic Variation & Climate Adaptation in Quercus macrocarpa
Mapping genome-wide variation in wild bur oak populations. Utilizing de novo genome assemblies, GWAS of adaptive leaf morphology and phenology traits, and gene-environment interactions to calculate genomic offset metrics and identify climate-resilient seed sources.
- Methodology: de novo Genome Assembly, GWAS, Genotype-Environment Association (GEA)
- Focus: Deciduous oak resilience & adaptive trait selection
Comparative Systems Biology of Populus trichocarpa Traits
Applying Bayesian hierarchical models and deep learning frameworks to decode comparative systems biology in black cottonwood. Unifying multi-omic genetic and transcriptomic datasets to predict complex quantitative wood and biomass variations.
- Methodology: Bayesian Hierarchical Modeling, Multi-Omic Integration, Genomic Prediction
- Focus: Wood chemistry & bioenergy trait characterization