Chudi Zhong

Bio: Chudi Zhong is a Ph.D. candidate in computer science at Duke University, advised by Cynthia Rudin. Her research focuses on developing interpretable machine learning algorithms and pipelines to facilitate human-model interaction for high-stakes decision-making problems. Her work has been published in top-tier conferences (NeurIPS/ICML) and was selected as a finalist for the INFORMS Data Mining Best Student Paper Award. She won 2nd place in the 2023 Bell Labs Prize. Prior to her Ph.D., Chudi earned her bachelor’s degree in Statistics from UNC-Chapel Hill and a master’s degree in Statistics from Duke.

Talk Title: Towards Trustworthy AI: Interpretable Machine Learning Algorithms that Produce All Good Models

Abstract: Machine learning has been increasingly deployed for high-stakes decisions that deeply impact people’s lives. However, not all models can be trusted. To ensure the safe and efficient utilization of machine learning models in the decision-making process, we change both model training and evaluation steps within the standard machine learning pipeline. In this talk, I will introduce our new paradigm, which shifts from finding a single optimal model to enumerating all good interpretable models. This paradigm allows users an unprecedented level of control over model choice among all models that are approximately equally good. I will present algorithms used to find the set of all good models, discuss how this set enables practitioners to explore alternative models that might have desirable properties beyond what could be expressed within a loss function, and show applications.