Yuyao Wang

I am a final-year PhD student in mathematics with a specialization in statistics at University of California San Diego, advised by Professor Ronghui (Lily) Xu and collaborating with Dr. Andrew Ying. I also received mentorship from Dr. Kendrick Li during my 2024 summer internship at St. Jude Children's Research Hospital, where I worked on addressing the selection bias issue involved in the cancer survivorship study SJLIFE.

Before graduate school, I recieved my bachelor’s degree in mathematics from Xi’an Jiaotong University. I also visited Georgia Institute of Technology and University of Alberta as a visiting student during my undergrad.

Email    GitHub    Google Scholar    CV   

profile photo
Research

My research is in causal inference, survival analysis, missing data problems, and semiparametric theory, particularly in addressing challenges related to left truncation, a common source of selection bias (sampling bias) for time-to-event data.

I am broadly interested in topics in causal inference and missing data problems, especially in selection bias, heterogeneous treatment effects, balancing approaches, assumption violations, continuous treatment, and continuous time treatment, sensitivity analysis, distribution shifts, generalizability, and data integration. I am also interested in semiparametric and nonparametric theory, high dimensional statistics, and conformal inference.

I am broadly interested in applications in health sciences and social sciences.

Publications
  1. Yuyao Wang, Andrew Ying, Ronghui Xu. (2024) Doubly robust estimation under covariate-induced dependent left truncation. Biometrika, 111(3), 789-808. [pdf] [code] [R package]

    (This paper won the student paper competition award for 2023 Lifetime Data Science Conference)

  2. Yingwei Peng, Yuyao Wang, Ronghui Xu. (2023) Measures of explained variation under the mixture cure model for survival data. Statistics in Medicine, 42(3), 228-245. [pdf]
ArXiv Preprints and Working Papers
  1. Yuyao Wang, Andrew Ying, Ronghui Xu. (2024) Learning treatment effects under covariate dependent left truncation and right censoring. arXiv:2411.18879. [pdf]
  2. Lon R. White, Yuyao Wang, Lenore Launer, Lucia Seale, Marla Berry, Margaret Flannagan, Thomas J. Montine, Ronghui Xu. (2024+) Midlife dietary selenium and Okinawan ancestry are independently associated with less severe Alzheimer neuropathology at autopsy in Japanese-American men. Submitted to Journal of Alzheimer's Disease.
R packages
  1. truncAIPW: Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation
  2. aftR2: R-squared Measure under Accelerated Failure Time (AFT) Models

Presentations

Talks

  • Learning Treatment Effects under Covariate Dependent Left Truncation and Right Censoring.
    • Online Causal Inference Seminar (OCIS), November 5, 2024. [slides] [video]
    • Biostatistics Seminar, Department of Population Medicine, Harvard Pilgrim Health Care Institute, 2024.
    • Causal Inference Seminar, Boston University, 2024.
    • Causal Group Seminar, Carnegie Mellon University, 2024.
    • Joint Statistical Meetings, 2024.
    • Southern California Applied Mathematics Symposium, 2024.
  • Doubly Robust Estimation under Covariate-induced Dependent Left Truncation.

Posters

  • Doubly Robust Estimation of Treatment Effects under Covariate Dependent Left Truncation and Right Censoring.
    • American Causal Inference Conference (ACIC), 2024. [poster]
    • Public Health Research Day at UCSD, 2024.
  • Multiply Robust Estimation of Treatment Effect for Time-to-event Outcome under Dependent Left Truncation.
    • American Causal Inference Conference (ACIC), 2023. [poster]
    • Public Health Research Day at UCSD, 2023.
  • Semiparametric Estimation for Non-randomly Truncated Data.
    • American Causal Inference Conference (ACIC), 2022. [poster]
    • Public Health Research Day at UCSD, 2022.

Template from here.