Lijia Zhou (周里佳)

Hi! I am currently a quantitative researcher at Chicago Trading Company.

I obtained my Ph.D. in Statistics from the University of Chicago in June 2023. I was extremely fortunate to be advised by Professor Nathan Srebro. I spent most of my Ph.D. improving the fundamental theoretical tools (e.g., Rademacher complexity and uniform convergence bounds) to better understand the generalization behavior of high-dimensional statistical models, with applications to learning problems such as kernel regression, phase retrieval, and low-rank matrix sensing. My thesis provides a principled approach to studying minimal norm interpolation and quantifying the effect of overfitting in statistical learning.

Before graduate school, I completed my undergraduate study in Applied Mathematics (B.S.) and Statistics (B.S.) also from UChicago. I spent a year at UCLA studying mathematics before transferring to Chicago. I am originally from Guangzhou, China.