Ryan is a graduate student in Environmental Science and Engineering, previously studying computer science at Princeton University. Since he was a kid, Ryan has always been fascinated in weather and climate, and through his undergraduate, he focused on ways to apply advances in computer science and machine learning to climate science. His past work involved explaining north Atlantic hurricane frequencies and machine learning-based methods for data assimilation and modeling of hurricane wind fields. For his PhD, he is interested in some of the fundamental questions in climate science, like explaining rainfall, heat, and cloud feedback patterns and trends using a combination of theory, modeling, and observations. He's also interested in climate modeling, and is looking to apply his background in computer science and applied math to better understand how anthropogenic climate change will impact the fundamental state of our climate.
