On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections. - Peizhao Li; Yifei Wang 002; Han Zhao 002; Pengyu Hong; Hongfu Liu 001 (ICLR, 2021)

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning. - Justin Li; Dakang Zhang; Yifei Wang 002; Christopher Ye 002; Hao Xu; Pengyu Hong (CoRR, 2021)

Knowledgebra: An Algebraic Learning Framework for Knowledge Graph. - Tong Yang; Yifei Wang 002; Long Sha; Jan Engelbrecht; Pengyu Hong (Mach. Learn. Knowl. Extr., 2022)

Counterpart Fairness - Addressing Systematic between-group Differences in Fairness Evaluation. - Yifei Wang 002; Zhengyang Zhou; Liqin Wang; John Laurentiev; Peter Hou; Li Zhou 007; Pengyu Hong (CoRR, 2023)

Asymmetric Contrastive Multimodal Learning for Advancing Chemical Understanding. - Hao Xu; Yifei Wang 002; Yunrui Li; Pengyu Hong (CoRR, 2023)

Motif-Based Graph Representation Learning with Application to Chemical Molecules. - Yifei Wang 002; Shiyang Chen 004; Guobin Chen; Ethan Shurberg; Hang Liu 001; Pengyu Hong (Informatics, 2023)

Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases. - Yifei Wang 002; Liqin Wang; Zhengyang Zhou; John Laurentiev; Joshua R. Lakin; Li Zhou 007; Pengyu Hong (J. Biomed. Informatics, 2024)

Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning. - Yifei Wang 002; Yunrui Li; Lin Liu; Pengyu Hong; Hao Xu (J. Chem. Inf. Model., 2025)