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 develops statistical theory and methodology for uncovering \textbf{latent structure} in complex data. A unifying theme is to make latent structure and representation learning identifiable, interpretable, computationally scalable, and statistically reliable.

• Identifiable deep generative models and causal representation learning: I study identifiability, latent graph discovery, and causal representation learning in nonlinear probabilistic graphical models with latent structures.
• High-dimensional inference for latent structure: The high dimensionality and the latent structure pose double statistical challenges. I develop spectral, tensor, and likelihood-based methods for latent class, mixed-membership, and nonlinear low-rank representation problems, with finite-sample theory and uncertainty quantification.
• Latent variable models for psychometrics, heterogeneous populations, and AI evaluation: Principled latent trait models for educational, psychological, biomedical, and language-model data, including cognitive diagnosis, item response theory, and psychometric frameworks for evaluating large language models (LLMs).

Here is my Curriculum Vitae (CV).