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.

My research broadly focuses on theory and methodology in scenarios where the distribution is weakly specified or fully supervised data are unavailable. I am particularly interested in statistical inference with adaptively collected data, distribution-free inference under distribution shifts, and learning with multi-modal data.

research interests

• Distribution-free inference
  Uncertainty quantification for black-box models, including reliable deployment of foundation model outputs with statistical guarantees (GJR24, GJNR25).

• Selective inference under distribution shift
  Valid inferential procedures in the presence of selection bias, distribution shift, missingness, and censoring (GBM23, GBM24, GHRB24).

• Statistical learning with multi-modal data
  Theoretical foundations for modern (self-supervised) representation learning techniques (GMM25, GMZ23).

• Observational studies
  Efficient statistical methods for identifying cause-and-effect relationships in complex real-world settings.