We design statistical models and computational tools that turn high‑dimensional, complex‑structured data into clinically meaningful insight.
What we do? Create novel, interpretable, and useful features from complex data.
We generate and derive feature sets that capture signal in digital data, spatial‑omic data, geospatial data, and imaging data, providing surrogate representations of complex data that power downstream discovery and decision‑making.
Predict clinical phenotypes.
We integrate biomarkers derived from complex‑structured data to build statistical and machine learning models to predict clinical characteristics that matter to patients and providers.
We develop innovative statistical methodology that turns the structure in data into signal—explicitly leveraging complexities of data from modern biomedical technologies.
Hierarchical Bayesian modeling to encode domain knowledge and to propagate uncertainty. Variable selection to pinpoint a parsimonious set of biomarkers from high‑dimensional feature sets. Geometric & functional data analysis to analyze shapes, curves, and trajectories. Spatial statistics to exploit spatial dependence in data. Network models in statistics & machine learning to learn interaction structure and to infuse graph constraints.