I am Hugh Zheng, a Ph.D. candidate in Statistics at the University of Florida (UF), advised by Dr. Leo L. Duan. I publish as Yu Zheng.
I am drawn to statistical problems that are at once mathematically challenging and scientifically motivated. In particular, I am interested in both methodology and theory related to combinatorial problems and structured data, such as clustering, variable selection, and integer-valued data under combinatorial constraints (e.g. matching data). I am deeply interested in both method innovation as well as asymptotic theory for statistical and machine learning methods. My research outputs in the past few years include statistical modeling of combinatorial response data (JASA, accepted), clustering consistency for Bayesian spanning-forest models (Bernoulli, revision submitted), and anti-correlation Gaussian data augmentation for L1-ball-type models such as soft-thresholded Gaussian process models (JCGS, 2025).
Beyond statistical research, I am interested in software development and AI engineering. The goal is to incorporate reusable code and AI tools into my workflows for better efficiency and performance, while also helping the community. I maintain open-source tools including latex2arxiv, latex2ufdissertation,academic-application-tracker, combinatorial-regression, combreg (R package) and Anti-correlation-Gaussian.
Research Interests
- Asymptotic theory (ergodicity, consistency, convergence rates)
- Model-based clustering
- Markov chain Monte Carlo (MCMC)
- Combinatorial and structured data
- Graphical models
- Variable seletion
Education
Ph.D. in Statistics (Ongoing)
University of Florida
B.S. in Mathematics and Applied Mathematics, 2016-2020
University of Science and Technology of China
Work Experience
Quantitative Research Intern, Jun 2024 - Aug 2024
Susquehanna International Group (SIG)