Peng Zhao
pzhao [at] udel [dot] edu
214 Townsend Hall
Newark, DE 19716
I am an Assistant Professor in the Department of Applied Economics and Statistics at the University of Delaware and a resident faculty member of the Data Science Institute.
My research develops a theory of deattenuation and anti-shrinkage. I study when statistical procedures systematically attenuate recoverable signal, and when undoing—or even reversing—that attenuation improves prediction or estimation because the signal-recovery gain exceeds the accompanying variance cost.
Before joining Delaware, I was a postdoctoral researcher in Statistics at Texas A&M University. I received my Ph.D. in Statistics from Florida State University, advised by Yiyuan She and Yun Yang.
My research is currently supported in part by Microsoft Research through the Graph Machine Learning project, where I serve as a co-PI.
For a complete record, please see my publications or download my CV.
News
| Aug 07, 2026 | New blog post: The Spectrum Was Known to Be Biased. Why Did PCR Keep Inverting It? — de-floored PCR and correcting the spectral floor before inversion. |
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| Aug 06, 2026 | New blog post: Shrinkage Is Not a Universal Law — when expansion, rather than shrinkage, is the right correction in overparameterized prediction. |
| Jul 24, 2026 | New preprint: Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent. |
| Jul 18, 2026 | New preprint: De-floored Principal Component Regression: When Rank Selection Alone Is Insufficient for Prediction. |
Selected Publications
- An Approximate Bayesian Approach to Covariate-dependent Graphical ModelingBernoulli, 2026Accepted
- Robust High-Dimensional Covariate-Assisted Network ModelingarXiv preprint arXiv:2505.02986, 2025
- Structured Optimal Variational Inference for Dynamic Latent Space ModelsJournal of Machine Learning Research, 2024