About me
I am an applied mathematician and aspiring climate scientist. I recently completed a PhD in Computational and Applied Mathematics (CAM) at the University of Chicago, and in September 2022 began a Postdoctoral position at MIT in the department of Earth, Atmospheric, and Planetary Sciences. My supervisor is Paul O'Gorman. I study the dynamics and predictability of extreme weather events and climate tipping points, using frameworks and methods borrowed from computational chemistry. In particular, I am working on developing rare event sampling methods for extreme precipitation events. My primary PhD advisor was Jonathan Weare at NYU's Courant Institute. Additionally, I also collaborate with Edwin Gerber (also at Courant), Dorian Abbot in UChicago Geophysical Sciences, Mary Silber in UChicago CAM, and Aaron Dinner in UChicago chemistry.
Extreme weather events and climate tipping points are enormously consequential, but very hard to study through direct simulation, precisely because they are rare. My overarching goal is to get the most out of the available data by combining dynamical insight, numerical simulation, and data-driven machine learning methods to characterize extreme events. My PhD focused on Sudden Stratospheric Warming (SSW), a spectacular breakdown of the stratospheric polar vortex that can wreak havoc on the Chicago winter. SSWs are rare, sudden, and hard to predict. Using transition path theory and Markov state models, we are establishing concrete metrics of risk, predictability, and variability of SSW events across a hierarchy of models. I am looking forward to exploring other areas of atmospheric science, in particular extreme precipitation and heat waves.
I had the great fortune to be supported by the initiative on Climate Grand Challenges at MIT. During graduate school, I was supported by the Department of Energy Computational Science Graduate Fellowship (DOE CSGF)