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,
Long-tail behavior
How can we ensure that a machine learning system won't fail catastrophically in the wild under changing conditions?
Understanding
A system which understands how to answer questions or generate text should also do so robustly out-of-domain.
Fairness
Machine learning systems which rely on unreliable correlations can result in spurious and harmful predictions.