About me Google Scholar CV Publications
Welcome!
I am an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign.
I am also a Core Member of the NSF-Simons AI Institute for the Sky (SkAI Institute).
My research studies foundational problems in modern machine learning and statistics:
- When do overparameterized models such as neural networks interpolate and still generalize?
- How can we quantify uncertainty for iterative methods and adaptive models with evolving data?
- How do learning and decision-making algorithms behave in high dimensions?
- How can we learn from structured or imperfect data?
To address these questions, I develop and use tools from high-dimensional probability and statistics, random matrix theory, information theory, optimization, and the analysis of iterative algorithms.
In a nutshell, it is always about:
\[y = f_\theta(X; \epsilon) \in \mathbb{R}^n, \qquad X \in \mathbb{R}^{n \times d}, \qquad \theta \in \Theta.\]But at least one of the above is unusual — Explore my research themes above!
Contact
chcheng (at) illinois (dot) edu
Bio
I am an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign. Prior to that, I was a postdoctoral scholar in the Department of Statistics at the University of Chicago, mentored by Professor Rina Foygel Barber, and completed my PhD in Statistics at Stanford University, jointly advised by Professor John Duchi and Andrea Montanari. I was a recipient of the William R. Hewlett Stanford Graduate Fellowship. I received my Bachelor’s degree in Computational Mathematics from Peking University.
Employment
- Assistant Professor, Department of Statistics, University of Illinois Urbana-Champaign, 2026.8 – Current
- Postdoctoral Scholar, Department of Statistics, University of Chicago, 2025.8 – 2026.8
- Research Intern, LinkedIn Corporation, 2023.6 – 2023.9
Education
- Ph.D. in Statistics, Stanford University, 2019.9 – 2025.6
- B.S. in Mathematics, Peking University, 2015.9 – 2019.6