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I am currently an assistant professor at the computer science department in Stanford university.
My research uses tools from statistics to make machine learning systems more robust and trustworthy — especially in complex systems such as large language models. The goal of my research is to use robustness and worst-case performance as a lens to understand and make progress on several fundamental challenges in machine learning and natural language processing. A few topics of recent interest are,
Almost one year after the “DeepSeek moment,” this brief analyzes China’s diverse open-model ecosystem and examines the policy implications of their widespread global diffusion.
This brief tests a variety of ordinary text prompts to examine how major text-to-image AI models encode a wide range of dangerous biases about demographic groups.
This brief warns that fair use may not fully shield U.S. foundation models trained on copyrighted data and calls for combined legal and technical safeguards to protect creators.
This brief introduces a quantitative framework that allows policymakers to evaluate the behavior of language models to assess what kinds of opinions they reflect.
This brief introduces a quantitative framework that allows policymakers to evaluate the behavior of language models to assess what kinds of opinions they reflect.
Extending the WILDS Benchmark for Unsupervised Adaptation
Extending the WILDS Benchmark for Unsupervised Adaptation
Large Language Models Can Be Strong Differentially Private Learners
Large Language Models Can Be Strong Differentially Private Learners