Tracking Monotonically Advancing Boundaries in Image Sequences Using Graph Cuts and Recursive Kernel Shape Priors. - Joshua C. Chang; K. C. Brennan; Tom Chou (IEEE Trans. Medical Imaging, 2012)

Cortical Spreading Depression: A Mathematical Model and Associated Inverse Problems. - Joshua C. Chang (2012)

Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior. - Joshua C. Chang; Tom Chou (J. Math. Imaging Vis., 2014)

Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models. - Joshua C. Chang; Shashaank Vattikuti; Carson C. Chow (CoRR, 2019)

A mathematical model for persistent post-CSD vasoconstriction. - Shixin Xu; Joshua C. Chang; Carson C. Chow; K. C. Brennan; Huaxiong Huang (PLoS Comput. Biol., 2020)

Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization. - Joshua C. Chang; Patrick Fletcher; Jungmin Han; Ted L. Chang; Shashaank Vattikuti; Bart Desmet; Ayah Zirikly; Carson C. Chow (ICLR, 2021)

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death. - Joshua C. Chang; Ted L. Chang; Carson C. Chow; Rohit Mahajan; Sonya Mahajan; Shashaank Vattikuti; Hongjing Xia (CoRR, 2022)

Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery. - Joshua C. Chang; Carson C. Chow; Julia Porcino (AISTATS, 2023)

Interpretable (not just posthoc-explainable) heterogeneous survivors bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions. - Hongjing Xia; Joshua C. Chang; Sarah Nowak; Sonya Mahajan; Rohit Mahajan; Ted L. Chang; Carson C. Chow (MLHC, 2023)

Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions. - Hongjing Xia; Joshua C. Chang; Sarah Nowak; Sonya Mahajan; Rohit Mahajan; Ted L. Chang; Carson C. Chow (CoRR, 2023)

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models. - Joshua C. Chang; Xiangting Li 001; Shixin Xu; Hao-Ren Yao; Julia Porcino; Carson C. Chow (CoRR, 2024)

Bayesian information theoretic model-averaging stochastic item selection for computer adaptive testing: compromise-free item exposure. - Joshua C. Chang; Edison Choe (CoRR, 2025)