About

Hi! I’m Julian, a third-year Ph.D. student in Environmental Science and Engineering at Caltech. I work with Tapio Schneider as a member of the Climate Modeling Alliance, where I’m applying mathematical tools and machine learning to tackle climate problems.

At CliMA, this includes developing a new framework for diagnosing and improving model calibration in the non-differentiable slow forward model setting. I’ve been applying this framework to CliMA’s atmosphere model, ClimaAtmos.jl, to develop more efficient loss functions for use in global calibrations. I’m also developing a new ML closure for the atmosphere model’s representation of clouds. Interested in the machine learning revolution in the numerical weather prediction world, I am exploring history and space tradeoffs in the Neural Operator setting.

Beyond research, I enjoy coordinating weekly trail runs for the Caltech Alpine Club in the beautiful San Gabriel Mountains, where I get to enjoy both the outdoors and the awesome local running community.

Before joining Caltech, I completed my Applied Mathematics degree at Harvard University in 2023, where I had the opportunity to work on diverse projects spanning high-performance computing, climate modeling, and statistical methods. I collaborated with Marine Denolle on seismic waveguide analysis using Julia and AWS, with Mimi Hughes, Nathaniel Johnson, and Kai-Chih Tseng on snow drought climatology, and with Kelly McConville on forest carbon estimation. I also worked briefly as a software engineer at Coolant, developing machine learning tools for carbon stock quantification.