Speaker: Fei Wang, Ph.D. Associate Professor, Department of Population Health Sciences, Weill Cornell Medicine
Faculty Host: Hye-Chung Kum
Abstract: Longitudinal patient clinical data, such as electronic health records (EHR), are with better availabilities in recent years. This provides an unprecedented opportunity for data mining and machine learning researchers to design appropriate computational algorithms and dig clinical insights. This talk will showcase several examples on such direction and demonstrate the promise of modern machine learning models for clinical risk prediction, and discuss the potential challenges and future directions.
Biography: Dr. Fei Wang is an Associate Professor of Health Informatics in Department of Population Health Sciences at Weill Cornell Medicine. His major research interest is data mining, machine learning and their applications in health data science. He has published more than 250 papers on top venues in AI (such as AAAI, KDD, ICML, NeurIPS), clinical medicine (such as JAMA Internal Medicine and Annals of Internal Medicine) and health informatics (such as AMIA and JAMIA). His papers have received around 15,000 citations and his Hindex is 60. His papers have won 7 best paper awards on international conferences including SDM, ICDM and AMIA. He has received numerous awards including NSF CAREER award, IEEE International Conference on Healthcare Informatics (ICHI) leadership award, as well as the awards from industries including Google faculty research award, Amazon machine learning for research award (twice) and Sanofi iDEA award. He has been the PI on numerous grants from federal agencies NSF, NIH, ONR and private foundations such as MJFF. Dr. Wang is a fellow of the American Medical Informatics Association (AMIA).
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