I lead a team that works directly at the nexus of health data science and machine learning leveraging various modalities of patient data to enable precision medicine at scale. We leverage a variety of multi-omic biomedical data, including: Electronic Health Records (EHR), clinical text, genomics, physiological waveforms, and images. We develop machine learning techniques to mine and effectively fuse these data for various tasks including predictive modeling, disease phenotyping, drug repurposing, and drug discovery. I am also interested in how we can transform messy real-world clinical data into robust evidence for regulatory decision-making.To promote generalizability of EHR work, I have developed open source software leveraging the OMOP common data model (romop.ucsf.edu, patientexplorer.ucsf.edu, & patientexplorer.cancergenetrust.com)Full publication list: https://scholar.google.com/citations?user=BIHeNNUAAAAJ&hl=enPersonal Github: https://github.com/BenGlicksberg/Prior Lab Guthub: https://github.com/Glicksberg-Lab/