Deriving Holistic Insights from Disconnected Biomedical Datasets
Biomedical data are scattered and disconnected. Different cohorts are created with different hypotheses and thus different information modalities are collected. It is thus challenging to derive holistic disease related insights from these disconnected data sets. In this talk, I will present our recent research on developing different machine learning approaches to achieve this goal, as well as their applications in different disease contexts.
Bio: Fei Wang is currently the Associate Dean of Data Science and Artificial Intelligence at Weill Cornell Medicine, where he is also the Frances and John Leob Professor of Medical Informatics and Division Chief of Health Informatics and Artificial Intelligence in Department of Population Health Sciences (primary), and a Professor in Department of Emergency Medicine (secondary). Wang is the founding director of the WCM Institute of AI for Digital Health, and the founding co-director of the WCM Data Coordination Center. He is also a senior technical advisor at New York Presbyterian hospital, a senior faculty fellow of clinical artificial intelligence at Cornell Tech, and an adjunct scientist at Hospital for Special Surgery. His research interest is machine learning and artificial intelligence in biomedicine. Wang has published over 350 papers on the major venues of AI and biomedicine, which have received more than 40K citations to date. His H-index is 91. He is an elected fellow of American Medical Informatics Association, American College of Medical Informatics and International Academy of Health Sciences and Informatics, and a distinguished member of Association for Computing Machinery.