Colloquium - Ying Jin - April 09, 2026

Speaker: Ying Jin, University of Penn
Date/Time: Thursday, April 09, 2026, 10:00 AM - 11:00 AM ET

Title: Conformal Selective Prediction with General Risk Control

Abstract: In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted.  We propose Selective Conformal Risk control with E-values (SCoRE), a new framework for deriving such decisions for any trained model and any user-defined, bounded and continuously-valued risk. SCoRE offers two types of guarantees on the risk among ``positive'' cases in which the system opts to trust the model. Built upon conformal inference and hypothesis testing ideas, SCoRE first constructs a class of (generalized) e-values, which are non-negative random variables whose product with the unknown risk has expectation no greater than one. Such a property is ensured by data exchangeability without requiring any modeling assumptions. Passing these e-values on to hypothesis testing procedures, we yield the binary trust decisions with finite-sample error control. SCoRE avoids the need of uniform concentration, and can be readily extended to settings with distribution shifts. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models.

Bio: Ying Jin is an Assistant Professor in Statistics and Data Science at the Wharton School, University of Pennsylvania. Prior to that, she was a Wojcicki-Troper Postdoctoral Fellow at Harvard Data Science Initiative from 2024 to 2025. She obtained her PhD in Statistics from Stanford University in 2024, advised by Professors Emmanuel Candès and Dominik Rothenhäusler. Her research centers around statistical uncertainty quantification for black-box AI models, generalizability, distributional robustness, causal inference, and their applications in biomedical discovery and human decisions.

Website: https://ying531.github.io/