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:

  • Deep generative models and representation learning with latent variables: I study identifiability and other essential properties inherent in deep nonlinear models and 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 with theoretical guarantees to recover latent structures and quantify uncertainty.
  • Latent variable modeling in psychometrics and beyond: 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.

My research is partially supported by NSF Grant DMS-2210796 (sole PI).