The research is supported by NIH, PCORI, NSF, ARPA-H, VA, and AHA.
Research Experience
Leads a research group focused on developing robust statistical and ML/AI methods for (integrative) analysis of big health data, including -omics data, EHRs, and imaging data. The work also covers missing data, causal inference, data privacy, algorithmic fairness, Bayesian methods, and clinical trials.
Background
Research interests include the development of statistical and machine learning/artificial intelligence methods for impactful biomedical research, with a focus on advancing precision medicine and population health through the integration of big health data such as -omics, EHRs, and imaging data.