Computational Multi-omics for Age-related Diseases
Our research focuses on computational modeling of human multi-omics data, integrated with other modalities such as medical imaging and EHR data, to uncover the mechanisms driving cardiovascular and other age-related diseases.
Key areas of interest:
Aging – somatic mutation – associated diseases: We are passionate about understanding the biology connecting aging, somatic mutations (in blood, i.e., clonal hematopoiesis, and in other tissues), and their associated diseases. Beyond mechanistically characterizing these links, we aim to discover interventions capable of breaking or modifying them across the three pillars.
Multi-modal medical information & machine learning: We use deep learning for imaging (human and mouse) and LLMs to process patient information from EHR. For both areas, we further integrate genomics data to discover biological insights and advance our understanding of aging–somatic mutation–associated diseases.
Omics methods development: While we apply multi-omics data to biological questions, we actively tackle fundamental methodological challenges inherent to the data itself (e.g., discordance between proteomics platforms, omics instruments, etc.).
Our research is largely strengthened by the extensive human and murine datasets we have access to, and we are deeply grateful to our collaborators and cohort PIs for their trust.
Congratulations to Qiayi! Her 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.
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. [paper]
Welcome Aram to the lab!
Welcome Mesbah and Yuxuan to the group!
Welcome Haodong and Mengyu to the group!