Bio

I am a Ph.D. candidate in Statistical Sciences at the University of Toronto, co-advised by Professors Linbo Wang and Dehan Kong. Prior to UofT, I completed an M.S. in Statistics at the University of Washington working with Amy Willis. I also spent a summer as a research intern at Emory University working with Steve Qin and a semester as a visiting student at UC Berkeley.

My research advances robust and trustworthy causal inference methods for complex observational data, especially in the presence of unmeasured confounding. By leveraging structured assumptions in longitudinal and high-dimensional settings and drawing from semiparametric theory and modern machine learning, I develop approaches that yield identifiable, interpretable, and actionable causal estimates. The long-term goal is to provide statistically principled and computationally efficient methods with built-in uncertainty quantification and diagnostics, enabling scientists and policy-makers to draw reliable conclusions from complex data.

I am on the academic or industrial job market 2025-2026.

News

Aug 2025	I will attend Joint Statistical Meetings (JSM) 2025 at Nashville and give a talk.
Nov 2024	Simultaneous Estimation of Multiple Treatment Effects from Observational Studies is accepted by Journal of Computational and Graphical Statistics.
Sept 2021	I received Connaught International Scholarship.
July 2021	Our paper is accepted by Nature Communications.
June 2021	I received my M.S. in Statistics from University of Washington.