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• Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
• Multiple Testing under Dependence via Graphical Models
• Structure-leveraged Methods in Breast Cancer Risk Prediction
• Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy
• Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution
• Developing a Utility Decision Framework to Evaluate Predictive Models in Breast Cancer Risk Estimation
• Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution
• Developing a Clinical Utility Framework to Evaluate Prediction Models in Radiogenomics
• Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction
• Multiple Testing under Dependence via Semiparametric Graphical Models
• Learning Heterogeneous Hidden Markov Random Fields
• New Genetic Variants Improve Personalized Breast Cancer Diagnosis
• Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models
• Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms
• Predicting Breast Cancer and Prostate Cancer Susceptibility from Single Nucleotide Polymorphisms
• Structure Learning of Undirected Graphical Models with Contrastive Divergence
• Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies
• High-Dimensional Structured Feature Screening Using Binary Markov Random Fields
• A Collective Ranking Method for Genome-wide Association Studies