Patrick Chao

Hello! I am a final-year Statistics Ph.D. student at the Wharton school, University of Pennsylvania, gratefully advised by Edgar Dobriban. I am broadly interested in understanding large models and AI safety. Currently, my research is on LLMs, diffusion models, and adversarial attacks.

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

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

Email  /  Github

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Jailbreaking Black Box Large Language Models in Twenty Queries
Patrick Chao, Alex Robey, Edgar Dobriban,
Hamed Hassani, George J. Pappas, Eric Wong
arXiv | Website | Github | Press Coverage

We propose PAIR, an automated method that uses a language model to systematically generate semantic jailbreaks for other language models, often in under twenty queries. PAIR is more computationally efficient than state-of-the-art methods by many orders of magnitude and only requires black-box access.

Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory
Patrick Chao, Edgar Dobriban

When estimating an average under distribution shifts, folk wisdom is to use something robust like the median. Surprisingly, under bounded (Wasserstein) shifts, we show that the sample mean remains optimal. For linear regression, we show that ordinary least squares remains optimal.

Adversarial Prompting for Black Box Foundation Models
Natalie Maus*, Patrick Chao*, Eric Wong, Jacob Gardner
ICML Frontiers in Adversarial Machine Learning Workshop 2023
arXiv | Blog Post | Github

Systematically finding adversarial prompts for generative image models like Stable Diffusion and text models like GPT.

Interventional and Counterfactual Inference with Diffusion Models
Patrick Chao, Patrick Blöbaum, Shiva Kasiviswanathan
ICML Counterfactuals in Minds & Machines Workshop 2023 (Oral)
arXiv | Github

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

AdaPT-GMM: Powerful and Robust Covariate-Assisted Multiple Testing
Patrick Chao, William Fithian
arXiv | Vignette | Talk Recording | Github

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

Different Definitions of Conic Sections in Hyperbolic Geometry
Patrick Chao, Jonathan Rosenberg
Involve Research Journal, 2018

We show the standard definitions of conic sections are not equivalent in hyperbolic geometry and we define generalized versions.

Wharton Teaching Assistant: Intro to Business Statistics (STAT 1010), Intro to Statistics (STAT 1110), Sports Analytics (STAT 4010), Data Collection and Acquisition (STAT 4100/7100), Python for Data Science (STAT 7770) (2020-2023)
Berkeley Seal Teaching Assistant, CS189: Machine Learning, Spring & Fall 2019

Teaching Assistant, Data 100: Principles and Techniques of Data Science, Fall 2018

Last updated October 2023.
Website template from Jon Barron.