Research and applications: The intended and unintended consequences of communication systems on general internal medicine inpatient care delivery: a prospective observational case study of five teaching hospitals. - Robert C. Wu; Vivian Lo; Dante Morra; Brian M. Wong; Robert Sargeant; Ken Locke; Rodrigo Cavalcanti; Sherman D. Quan; Peter G. Rossos; Kim Tran; Mark Cheung (J. Am. Medical Informatics Assoc., 2013)

Classification with Vertex-Based Graph Convolutional Neural Networks. - John Shi; Mark Cheung; Jian Du 001; José M. F. Moura (ACSSC, 2018)

Contrastive Structured Anomaly Detection for Gaussian Graphical Models. - Abhinav Maurya; Mark Cheung (ASONAM, 2018)

Pooling in Graph Convolutional Neural Networks. - Mark Cheung; John Shi; Lavender Yao Jiang; Oren Wright; José M. F. Moura (ACSSC, 2019)

Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification. - Lavender Yao Jiang; John Shi; Mark Cheung; Oren Wright; José M. F. Moura (ACSSC, 2020)

A Dual Approach to Graph CNNs. - John Shi; Mark Cheung; Wendy Summer; José M. F. Moura (ACSSC, 2020)

Graph Neural Networks for COVID-19 Drug Discovery. - Mark Cheung; José M. F. Moura (IEEE BigData, 2020)

Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology. - Mark Cheung; John Shi; Oren Wright; Lavender Yao Jiang; Xujin Liu 001; José M. F. Moura (IEEE Signal Process. Mag., 2020)

Graph Classification: Tradeoffs between Deep Neural Network Architecture and Graph Topology. - Mark Cheung; José M. F. Moura (ACSCC, 2021)

Using Sparse Spectral Shifts in Graph CNNs. - Austin Lin; Wendy Summer; John Shi; Mark Cheung; José M. F. Moura (ACSCC, 2021)

Geometric Deep Learning: Impact of Graph Structure on Graph Neural Networks - Mark Cheung (2023)

Reconstructing Fine-Grained Network Data using Autoencoder Architectures with Domain Knowledge Penalties. - Mark Cheung; Sridhar Venkatesan (CoRR, 2025)