Statistical Learning is a framework under the broad umbrella of Machine Learning that uses techniques from functional analysis to understand data. Statistical learning is often divided into two common categories: (i) supervised, and (ii) unsupervised learning. Briefly, supervised learning involves building a predictive model based on some response or outcome of interest; while, unsupervised learning learns about relationships and data structure without any supervising output variable. Many faculty in the Center are developing novel statistical learning approaches to tackle specific public health related problems. Some of these areas include: artificial neural networks for medical imaging, anomaly detection methods for clinical trials, online learning techniques for real-time clinical prognostics, and dimensionality reduction and structured prediction models in genome-wide association studies.