Uncertainty, Evaluation & Reliable Decision-Making

My research studies how intelligent systems can recognize uncertainty, identify potential failures, and support decisions about when to act, reject a prediction, or seek additional evidence.

This work connects statistical measures of uncertainty with practical evaluation methods. It spans classification and regression, generated-content assessment, and the privacy risks associated with trained models.

Our work on prediction errors leads to a practical question for auditors: what evidence should they collect next under a limited testing budget? Our position paper on language-model auditing examines this question in changing deployment contexts.