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, where I worked with Bani K. Mallick, Anirban Bhattacharya, and Debdeep Pati. 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
| Sep 24, 2026 | Our paper When Does Subspace Direction Matter for LoRA? Regime Analysis of the Magnitude Principle in Few-Shot Adaptation has been accepted to NeurIPS 2026. Congratulations to Nischal! |
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| Sep 16, 2026 | Welcome Ira to our research group! |
| Aug 31, 2026 | Our lab has been approved to join Anthropic’s Claude Team plan for Scientists. Thank you to Anthropic for supporting our research! |
| Aug 14, 2026 | New blog post: What Is Negative-Shifted Gradient Descent—When the Useful Estimator Is Not an Endpoint — how a finite-time signed path passes smoothly through the would-be endpoint pole and creates head anti-shrinkage with controlled lower-spectrum exposure. |
Selected Publications
- When Does Subspace Direction Matter for LoRA? Regime Analysis of the Magnitude Principle in Few-Shot AdaptationIn Advances in Neural Information Processing Systems (NeurIPS), 2026Accepted