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.
Prior to 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:-tʃiː/
. My name in Chinese is 顾雨琦.
My research revolves around unobserved latent structures in statistics, machine learning, psychometrics and other social and biomedical 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. One goal is to propose more interpretable models and discover potential causal explanations.
- High-dimensional statistics in the presence of latent structures: The high dimensionality and the latent structures pose double challenges to statistical analyses. I aim to develop rigorous estimation and statistical inference methods with theoretical guarantees. I am actively exploring spectral methods as one promising approach.
- 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 in biomedical sciences.
My research is partially supported by NSF Grant DMS-2210796 (sole PI).
I am looking for motivated students with a solid statistics and/or programming background to work with me.