- Phelps 1160
Title: Survival Analysis via Ordinary Differential Equations
Abstract: Survival analysis is an extensively studied branch of statistics with wide applications in various fields. Despite rich literature on survival analysis, the growing scale and complexity of modern data create new challenges that existing statistical models and estimation methods cannot meet. In the first part of this talk, I will introduce a novel and unified ordinary differential equation (ODE) framework for survival analysis. I will show that this ODE framework allows flexible modeling and enables a computationally and statistically efficient procedure for estimation and inference. In particular, the proposed estimation procedure is scalable, easy-to-implement, and applicable to a wide range of survival models. In the second part, I will present how the proposed ODE framework can be used to address the intrinsic optimization challenge in deep learning survival analysis, so as to accommodate data in diverse formats.
Bio: Weijing Tang is a PhD candidate in the Department of Statistics at the University of Michigan, advised by Prof. Ji Zhu. Her research interests include statistical machine learning, survival analysis, and statistical network analysis. She has received the ASA Nonparametric Statistics 2020 Student Paper Award, the ENAR 2021 Distinguished Student Paper Award, and the ASA Statistical Learning and Data Science Section 2021 Student Paper Award for her research work. Weijing is also enthusiastic about interdisciplinary research on applying statistical machine learning to help solve healthcare problems. Prior to the University of Michigan, Weijing received her BSc in Mathematics at Tsinghua University in 2016.