Rivas Lab aims to uncover the mechanisms of disease and improve health and well-being globally. By combining methods development and
biological and biomedical datasets, we develop statistical models and computational tools for the analysis of population-scale studies.

Scientific Themes

Generating effective therapeutic hypotheses

We’re mining large-scale human genetic, clinical outcomes, imaging, wearable sensors, and environmental data to generate effective therapeutic and preventative hypotheses for human diseases. Key disease areas of focus include: Crohn's disease, ulcerative colitis, chronic kidney disease, Alzheimer's disease, type 2 diabetes, coronary heart disease, and exploring additional diseases through electronic health records.

Genetic epidemiology

We are studying global distribution of common and rare disease predisposition genes. We have a strong interest in population isolates (e.g. Finland, French Canadians, Ashkenazi Jews), and the effect of admixture in Latin America.

High-dimensional methods development and optimization

We are developing high-dimensional statistical models for the interpretation and translation of genome sequencing data from common diseases and large  population biobanks and precision medicine/health initiatives. This will be the core of our research focus to address biomedical applications.


We are developing technologies for integrated learning healthcare systems with a particular focus on underserved communities and developing regions of the world. Currently, we are in the beginning stages of researching feasibility of digitizing clinical notes in underserved settings and the integration of this health information with genomic data.