Weijing Tang (CMU)

Math & Stats Department Colloquium

Friday, January 12th, 2023

Time: 2:30 p.m.  Place: Jeffery Hall, Room 234

Speaker: Weijing Tang (CMU)

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 an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University. Her research interests include statistical network analysis, machine learning, and survival analysis with applications to health and social sciences. She has received multiple awards for her research work, including the ASA Nonparametric Statistics, Statistical Learning and Data Science, and ENAR Distinguished Student Paper Awards. Prior to CMU, she was a Postdoctoral Researcher at Harvard University. She received her Ph.D. from the University of Michigan and a B.Sc. from Tsinghua University.