Since I was young, there have been three things that interest me the most: computers, aviation and the weather. Playing around with Flight Simulator 2004 on my parents’ PC furthered my curiosity, and my budding interest in meteorology was only increased through aviation and my early severe weather experiences.
Life in northern Illinois is chock-full of meteorological variability, with crippling January snowstorms and powerful June derechos that have lasting impacts on rural and urban areas. I have always been interested in how these events can be modeled and forecasted, and correlations between different variables have been my primary interest when entering graduate school. My main motivation has been applying deep learning techniques like convolutional and recurrent neural networks to model likelihoods of events and model climate trends, and to identify features over large scales or periods of time. These techniques are crucial to the future of meteorology and climatology, and can be implemented through Python scripts and mobile applications.
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