Advancing Biomedical Data Science: From Population Insights to Personalized Decisions
Presented by Ying Cui, PhD
Postdoctoral Scholar
Stanford University
* Candidate for faculty position in the Division of Biostatistics and Health Data Science
Rapid advances in biomedicine have enabled us to address important questions that were once intractable. There is a pressing need for analyzing massive data sets emerging from cutting-edge technologies, presenting challenges such as high-dimensionality and multi-modality. Additionally, there has been rising interests in personalized decision-making. Inspired by these challenges, my research aims to enhance the integration of statistical insights and data science innovations in biomedical research. In this talk, I will cover two projects.
The first part of the talk explores key questions about identifying covariates relevant to clinical outcomes of interest. Addressing these questions, however, can be complicated due to the presence of complex covariate effects. To tackle this problem, I developed a new testing and screening framework by adopting a global view via the novel concept of interval quantile independence. I showed that this general testing framework can naturally yield both unconditional and conditional screening procedures for ultra-high dimensional settings and enjoy the sure screening property.
In the second part of the talk, I address the feature selection problem from a personalized perspective. I designed a novel dynamic prediction rule to determine the optimal order of acquiring features in predicting clinical outcomes of interest for individual subjects. The goal is to optimize model performance while reducing the costs associated with measuring features. To achieve this, I employed reinforcement learning, where the agent decides the best action at each step: either making a final decision or continuing to collect new predictors. The proposed approach mirrors and improves real life decision-making processes, employing a “learn-as-you-go” paradigm.
A seminar tea will be held at 2:45 p.m. in University Office Plaza, Room 240.
