Computational Modeling of Multimodal Data in Aging, Somatic Mosaicism, and Cardiovascular Disease
Our research uses computational modeling to integrate multimodal biomedical data—including human multi-omics, medical imaging, and EHR data—to understand how aging and somatic mosaicism contribute to cardiovascular and other age-related diseases.
Key areas of interest:
Aging, somatic mosaicism, and disease: We investigate somatic mutations in blood—including clonal hematopoiesis—and other tissues, their relationships with aging and disease, and interventions that may modify these processes and their clinical consequences.
Multimodal phenotyping and machine learning: We use deep learning to analyze human and murine imaging data and LLMs to derive phenotypes from EHR data. We integrate these modalities with genomics and other omics data to uncover biological mechanisms and improve disease characterization.
Omics and statistical methods: We develop computational and statistical methods for multi-omics integration, cross-platform harmonization, quality assessment, and reproducible biological discovery.
Our research is enabled by access to extensive human and murine datasets. We are deeply grateful to our collaborators and cohort investigators for their partnership and trust.
Multiple papers accepted by Cell, European Heart Journal, Circulation, JACC: CardioOncology, Nature Communications (x2), and Hypertension!
Welcome Rong and Rusty to the lab!
Qiayi's work, The virtual longevity clinic: a multi-agent LLM estimates biological age, was selected for an oral presentation at the 2026 Biomarkers of Aging Conference (October 5–6)!
Congratulations to Meghana, Anthea, and Haodong on their paper published in Science Translational Medicine! The study was also featured by Harvard Medical School and Mass General Brigham, with additional coverage from Mass General Brigham on LinkedIn. [paper]
Welcome Aram to the lab!