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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,
In this brief, Stanford scholars test 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.
Foundation models are often trained on large volumes of copyrighted material. In the United States, AI researchers have long relied on fair use doctrine to avoid copyright issues with training data. However, our U.S. case law analysis in this brief highlights that fair use is not guaranteed for foundation models and that the risk of copyright infringement is real, though the exact extent remains uncertain. We argue that the United States needs a two-pronged approach to addressing these copyright issues—a mix of legal and technical mitigations that will allow us to harness the positive impact of foundation models while reducing intellectual property harms to creators.
Since the November 2022 debut of ChatGPT, language models have been all over the news. But as people use chatbots—to write stories and look up recipes, to make travel plans and even further their real estate business—journalists, policymakers, and members of the public are increasingly paying attention to the important question of whose opinions these language models reflect. In particular, one emerging concern is that AI-generated text may be able to influence our views, including political beliefs, without us realizing it. 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