Patrick Chao

Hello! I am a Statistics Ph.D. student at the Wharton school, University of Pennsylvania, gratefully advised by Edgar Dobriban. I am broadly interested in the statistical learning, diffusion models, distribution shift, adversarial attacks, and AI safety.

For my undergraduate education, I graduated from UC Berkeley, receiving a Bachelor's degree in Computer Science, Mathematics, and Statistics with honors. I was very fortunate to have been supervised by Will Fithian and Horia Mania.

Previously, I interned at Amazon AI, working on diffusion models for causality, and at Jane Street Capital as a trading intern.

Email  /  LinkedIn  /  Github

Attacking foundation models like Stable Diffusion and GPT by creating unusual prompts and getting unexpected outcomes.

Using diffusion models to answer interventional and counterfactual queries by modeling observational data, achieving state-of-the-art performance.

A powerful and robust multiple testing method using covariates to model the local false discovery rate by fitting a Gaussian mixture model.

Showing the standard definitions of conic sections are no longer equivalent in hyperbolic geometry.

Website template from Jon Barron.
Last updated February 2023.