Pengrun Huang (黄鹏润)
I am Pengrun Huang, a third-year Ph.D. student in the Computer Science and Engineering Department at the University of California, San Diego. I am fortunate to be co-advised by Kamalika Chaudhuri and Yu-Xiang Wang. Previously, I earned my bachelor’s and master’s degrees in Honors Mathematics from University of Michigan. I was mentored by Maggie Makar.
My research centers on the privacy and confidentiality of large language models (LLMs), from risk identification to defense. I am broadly interested in auditing what LLMs leak about their training data, developing principled metrics to measure memorization, and designing provable defense mechanisms to protect data privacy and detect unauthorized use.
Selected Publications
Alignment Defends LLMs from Property Inference Attacks
Preprint 2026
Can We Infer Confidential Properties of Training Data from LLMs?
NeurIPS 2025 (Spotlight 3%)
Conditional differential measurement error: partial identifiability and estimation
NeurIPS workshop CML4Impact 2022
