Our lab is focused on developing and applying advanced statistical and computational methods to enable a more meaningful interpretation of large-scale, high-throughput, multi-dimensional omic data. In particular, our long-term interest and goal is to elucidate and understand the systems-biology of oncogenesis (i.e. why do specific cells turn cancerous), and in parallel, to help develop cancer risk prediction tools that enable P4 Medicine strategies. To address these goals, we are using computational methods to (1) help map epigenetic and genetic alterations that accrue in normal cells as a function of age and exposure to major cancer risk factors, and (2) to help understand how these molecular alterations may lead to cancer development. As cell-type heterogeneity presents a major challenge, we are particularly interested in developing statistical methods to help dissect cell-type heterogeneity in both single-cell as well as bulk-sample contexts. We are adapting and pursuing methods from network/complexity science (network physics & graph theory), statistical mechanics, signal processing and machine learning, increasingly in the context of integrative multi-omic data.