Facheng Yu

I am a Ph.D. student at Department of Statistics, University of Washington, where I am fortunate to work with Zaid Harchaoui and Alex Luedtke on stochastic algorithms and causal inference for core AI. Previously, I obtained my M.S. in Statistics from UW and obtained my B.S. in Mathematics and Applied Mathematics from School of Mathematics and Statistics, Wuhan University. I am also fortunate to work with Yuqian Zhang on data integration.

My research interest lies in the intersection of machine learning, causal inference, core AI, and AI domain applications.

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Stochastic gradient optimization is the dominant learning paradigm for a variety of scenarios, from classical supervised learning to modern self-supervised learning. We consider stochastic gradient algorithms for learning problems whose objectives rely on unknown nuisance parameters, and establish non-asymptotic convergence guarantees.

Modern data analysis often involves integrating information from multiple sources, which can present challenges like data heterogeneity and imbalanced sample sizes. Our work introduces novel data integration techniques that rely only on external summary statistics to address these challenges and construct robust estimators. The framework is further extended to causal inference, facilitating the estimation of average treatment effects for generalizability and transportability.

• Institute for Foundations of Data Science (IFDS) Seminar, Oct. 2025, Seattle, USA.

• UW causal reading group, Dec. 2024, Seattle, USA.

My lecture notes as mentor of the statistic direct reading program and TA for STAT499: Undergraduate Research at University of Washington.

• Institute for Foundations of Data Science (IFDS) Scholarship, 2024.
• Excellent Student Scholarship, Wuhan University, 2020, 2021, 2022.