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My research primarily focuses on the development and implementation of machine learning techniques to help improve the accuracy, lead time, and frequency of convective and severe weather forecasts issued by the Storm Prediction Center. For example, one ongoing research project is focused on the development of a probabilistic model capable of predicting the likelihood of cloud-to-ground lightning occurring at a given location over time scales of 1 to 12 hours. A secondary area of interest is the development and implementation of alternative information dissemination methods with the hope to improve how the public receives and responds to severe weather information. On this front, I have worked to help develop and improve experimental, partially-automated Probabilistic Hazards Information (PHI) severe weather warning products as part of the Forecasting a Continuum of Environmental Threats (FACETs) initiative.