About me

I am a PhD candidate in Computational and Applied Mathematics (CAM) at the University of Chicago. I study the dynamics and predictability of extreme weather events and climate tipping points, using frameworks and methods borrowed from computational chemistry. My advisors accordingly span the range of disciplines. My primary advisor is Jonathan Weare at NYU's Courant Institute, and 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 goal, in my PhD and beyond, 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. In particular, I am studying 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'm fortunate to be supported by the Department of Energy Computational Science Graduate Fellowship (DOE CSGF)