Fri, Jun 24, 2022, 09:00 – 10:00AM (PDT), 12:00 – 1:00PM (EST)
The recent years have witnessed a surge of interests on mining evidence from electronic health records (EHR) to facilitate various tasks ranging from drug development to policy making. There are many challenges for analyzing EHRs such as complex confounding and population heterogeneity. In this talk, I will introduce several recent research from my lab on addressing these challenges in different disease contexts, and point out potential future directions.
Fei Wang is an Associate Professor at Department of Population Health Sciences, Weill Cornell Medicine, Cornell University. His major research interest is data mining, machine learning and their applications in health data science. He has published on the top venues of related areas such as ICML, KDD, NeurIPS, AAAI, JAMA Internal Medicine, Annals of Internal Medicine, etc. His papers have received over 20,300 citations so far with an H-index 69. His (or his students’) papers have won 8 best paper (or nomination) awards at international academic conferences. His team won the championship of the NIPS/Kaggle Challenge on Classification of Clinically Actionable Genetic Mutations in 2017 and Parkinson’s Progression Markers Initiative data challenge organized by Michael J. Fox Foundation in 2016. Dr. Wang is the recipient of the NSF CAREER Award in 2018, the inaugural research leadership award in IEEE International Conference on Health Informatics (ICHI) 2019, Amazon Machine Learning for Research Award in 2017 and 2019, Google Faculty Research Award in 2020, Sanofi iDEA Award in 2021. Dr. Wang was the chair of the Knowledge Discovery and Data Mining working group in American Medical Informatics Association (AMIA) in 2018-2019. Dr. Wang’s research has been supported by funding agencies including NSF, NIH, ONR, PCORI and MJFF. Dr. Wang is a Fellow of the American Medical Informatics Association (AMIA), a Fellow of the International Academy of Health Sciences and Informatics (IAHSI), and a Distinguished Member of the Association for Computing Machinery (ACM).