Epigenetics is recognized as one of the most important emergent fields in Computational Biology. The study of epigenetic mechanisms in development and disease using high-throughput techniques has been one of the most active areas in life and clinical sciences in the last five years. In this talk, I will present advances in statistical learning methods and data visualization for computational epigenomics and the fundamental discoveries of molecular mechanisms in cancer facilitated by these tools. Along the way, I will describe novel methods for (a) detecting genomic regions of significant epigenomic modification in cancer based on data smoothing methods, (b) learning of genomic predictive signatures based on modeling hyper-variability, and (c) systems and tools for effective computational and visual interactive exploratory data analysis.