Chudi Zhong presentation

Abstract: Machine learning has been increasingly deployed for high-stakes decisions that deeply impact people's lives. My research focuses on developing interpretable algorithms and pipelines to ensure the safe and efficient utilization of machine learning models in the decision-making process. In this talk, I will introduce a new paradigm, called learning the Rashomon set, which finds and stores all models within epsilon of the optimal loss. I will present algorithms for finding optimal models and Rashomon sets, discuss how this new paradigm can break the interaction bottleneck between users and ML algorithms, and provide examples of its applications.

Bio: Chudi Zhong is a PhD candidate in computer science at Duke University. 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 (NeuIPS/ICML) and was selected as a finalist for the INFORMS Data Mining Best Student Paper Award. Additionally, she won 2nd place in the prestigious 2023 Bell Labs Prize, and was selected as one of the 2023 Rising Stars in Data Science.