معرفی
Yeying Zhu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Her research focuses on causal inference, machine learning applications in networks, and statistical methodologies for handling missing data and confounding variables. She holds a Ph.D. in Statistics from The Pennsylvania State University (2013), an M.S. in Statistics & Applied Probability from the National University of Singapore (2008), and a B.S. in Statistics with a minor in International Economics & Trade from East China Normal University (2006).
Her work bridges theoretical statistics with practical challenges in causal analysis, including mediation analysis, propensity score modeling, and network treatment effects. Recent research explores UAV-enabled integrated networks using reinforcement learning and anomaly detection in satellite systems through ensemble learning techniques. She has contributed to advancements in high-dimensional causal mediation analysis and semi-parametric estimation techniques.
Notable areas of contribution include:
- Causal inference frameworks for non-ignorable missing data
- Machine learning-driven solutions for UAV trajectory optimization and satellite anomaly detection
- Development of robust statistical methods in longitudinal data and instrumental variable models
Her articles reflect interdisciplinary collaborations spanning statistics, computer science, and engineering, emphasizing methodological innovation and real-world applicability.
