Yu Gui

I am a Postdoctoral Researcher in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania, working with Professor Dylan Small and Professor Zhimei Ren.

I obtained my PhD in Statistics at the University of Chicago, where I was fortunate to be advised by Professor Rina Foygel Barber and Professor Cong Ma.

Prior to my PhD, I graduated from School of the Gifted Young at University of Science and Technology of China and was a student research intern advised by Professor Jun S Liu at Harvard.

I am broadly interested in statistical theory and methodology in scenarios where the distribution is weakly specified or fully supervised data are unavailable, motivated by problems that both arise from and can further inform real-world applications.

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research interests

• Adaptive inference in observational studies
  Valid and adaptive statistical methods for identifying cause-and-effect relationships in complex real-world settings, addressing challenges of heterogeneity, multiplicity, unmeasured confounding, and selection bias (GSR26).

• Distribution-free and selective inference
  • Uncertainty quantification for black-box models, including reliable and interactive deployment of foundation model outputs with statistical guarantees (GJR24, GJNR25).
  • Robust inferential procedures in the presence of selection bias, distribution shift, missingness, and censoring (GBM23, GBM24, GHRB24).
• Statistical learning with multi-modal data
  Theoretical foundations of modern (self-supervised) representation learning techniques (GMM25a, GMZ23) and efficient methods for disentangling shared and modality-specific features in single-cell multi-omics applications (GMM25b).

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