David’s research interests center around the development, testing, and implementation of machine learning procedures in operational meteorology. In particular, David has utilized machine learning techniques to develop probabilistic guidance to help SPC forecasters predict the timing and location of lightning occurrence, plan convective watch placement up to 48 hours in advance of a severe weather day, and estimate winter precipitation type in real-time. David is also involved in web development, and has created several web displays used internally at the SPC to view and process experimental products.