Despite having sequenced the human genome over fifteen years ago, much is still unknown about how it functions. With the advent of high-throughput genomics technologies, it is now possible to measure properties of the genome across the entire genome in a single experiment, such as measuring where a given protein binds to the DNA or what genes are expressed. However, the complexity and massive scale (billions of base pairs with thousands of measurements each) of these data sets pose challenges to their analysis. My research focuses on the development of new machine learning methods that address the challenges posed by genomics data sets. I am particularly interested in understanding gene regulation and its relationship to the genome’s 3D conformation in the nucleus, using machine learning methods based on probabilistic graphical models, submodular optimization and deep neural networks.