Yuqi Gu

Email: yuqi.gu@columbia.edu.
Address: Room 928 SSW, 1255 Amsterdam Avenue, New York, NY 10027

I am an Assistant Professor in the Department of Statistics at Columbia University. I am also a member of the Data Science Institute. Before joining Columbia in 2021, I spent a year as a postdoc at Duke University, mentored by David B. Dunson. In 2020 I received a Ph.D. in Statistics from the University of Michigan, advised by Gongjun Xu. In 2015 I received a B.S. in Mathematics from Tsinghua University. My first name can be pronounced as /ju:-tʃi:/. My name in Chinese is 顾雨琦.

My research centers around investigating unobserved latent structures widely present in statistics, machine learning, psychometrics and other applications:

• Identifiable and interpretable deep generative models and causal representation learning: I study identifiability and other essential properties of deep nonlinear probabilistic graphical models with latent representations. One goal is to propose more interpretable models and discover potential causal explanations.
• High-dimensional statistics with latent structures: The high dimensionality and the latent structures pose double challenges to statistical analyses. I aim to develop computationally efficient and statistically accurate methods, such as spectral methods and tensor methods, with theoretical guarantees to recover latent structures.
• Latent variable modeling in psychometrics and other applications: I develop principled statistical methods and theory to model educational and psychological data with substantively meaningful latent traits such as skills, attitudes, etc. I am also interested in other applications of latent variable modeling in biomedical sciences.

Here is my Curriculum Vitae (updated in August 2025).