I'm happy to announce that I recently joined BrightBand and am thrilled to work under the leadership of Ryan Keisler, Daniel Rothenberg, and Julian Green. We are going to build the most adaptive and efficient weather forecasting system in the world.
I am was a PhD student at
ETH Zürich where I studied machine learning.
Towards the end of my PhD, I interned with Stephan Hoyer for one year, advancing the state-of-the-art of machine learning for computational fluid dynamics, which resulted in this paper published in TMLR. This sparked my interest in ML for physical simulation and weather. I learned so much from Stephan, Dmitrii Kochkov, and the rest of the team.
I am currently writing I wrote my dissertation
and am looking for found my next position working at Ai2, developing pure ML-based climate
models under the leadership of Chris Bretherton. What a strong crew of climate scientists at Ai2! I learned a ton about scientific software development and contributed to the groundbreaking first pure AI-based climate model.
My research interests are quite broad: convex optimization, Bayesian inference (variational inference), simulation, inverse problems, probabilistic programming and more. My favorite applications are in the natural sciences. In recent years, I've focused on ML for weather and climate.
When I'm not working on these projects, you can find me playing a beautiful, one of a kind, yes that's right, Pete Ross banjo.
Most of them can be found on Google Scholar.
I am passionate about programming. Most of my code is on GitHub: github.com/gideonite.
You can also follow me on Twitter @gideoknite.
Email me and I will be happy to send you a copy.
"me" @ this domain name