Benjamin earned both his bachelor’s and master’s degree at the University of Texas-Pan American, where his dissertation focused on testing the effectiveness of virtual reality learning for training military personnel. He is currently pursuing a PhD in Industrial Engineering at Georgia Tech. The focus of his research is in using Big Data analytics to develop models for diagnosing faults of industrial assets such as automotive engines and gas turbines. His current work consists of diagnosing multiple, interacting faults in the automotive engine start system using ensemble methods and diagnosing key faults in the gas turbine such as lean blowout of the combustor flame, combustor centrebody degradation, and debits in coolant flow. For all these projects, Benjamin is leveraging modern sensing capabilities to acquire large, information-rich data sets. Using a combination of state-of-the-art signal processing and machine learning techniques, robust features correlated with machine degradation are extracted from these data sets and used for diagnosis. Through these experiments, Benjamin aims to demonstrate the diagnostic capability of Big Data analytics to the Gas and Power community.