I am a fifth-year PhD candidate at Johns Hopkins University in the Lundquist Group. My research focuses on improving model representations of meteorological processes relevant to wind farms, with particular emphasis on boundary-layer dynamics, mesoscale-microscale coupling, and the practical constraints of measurement siting.

Over the past seven years, I have worked across the energy ecosystem in industry, national laboratories, and universities. Across these domains, I have addressed pressing problems spanning energy markets, grid reliability, resource characterization, and technology development. These combined perspectives allow me to bridge gaps between model development, regulatory considerations, and the on-the-ground realities of resource assessment.

Modeling is central to my work. I have extensive experience with the core domain tools used in both academic research and industry practice, and I have developed internal best-practice guidelines informed by my time benchmarking wind energy estimates at the National Renewable Energy Laboratory. Beyond applying existing tools, I build and evaluate new modeling frameworks that integrate machine learning, atmospheric dynamics, numerical weather prediction, and graph-based methods.

I am currently seeking opportunities where I can apply this multidisciplinary expertise to advance offshore wind resource assessment, grid integration strategies, and the translation of atmospheric science into actionable tools.