学术报告
Deep neural networks for survival analysis: a few recent developments
题目：Deep neural networks for survival analysis: a few recent developments
报告人：Shuangge Ma (Yale University)
摘要：Most of the existing data analysis techniques are based on regression models and corresponding likelihood (estimating equation, U-statistic, etc.) estimations. In the past decade, we have witnessed a surge in deep neural network techniques. Despite promising performance (especially in prediction and accommodation of nonlinear relationships), they have often been criticized for not having lucid interpretations, satisfactory statistical properties, and valid inference. In this study, we report several recent developments in DNNs for censored survival data that have been driven by sound statistical principles to directly address the aforementioned limitations.
报告人简介： Dr. Shuangge (Steven) Ma is a Professor of Biostatistics at Yale School of Public Health. His research interests include genetic epidemiology, EHR data analysis, cancer biostatistics, and deep learning. He obtained his Ph.D. in Statistics from the University of Wisconsin, Madison in 2004. He was a Postdoctoral Fellow at the University of Washington, Seattle between 2004 and 2006 and has been at Yale University afterwards.
报告地点：教二楼613
联系人：胡晓楠
