ali-ramadhan/diffeqflux.jl

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

I'm an Applied Scientist at Afresh working on reducing food waste, a huge contributor to climate change, by using machine learning to help grocery stores optimize their inventory management and ordering decisions.

Before that I received my PhD in Computational Earth, Atmospheric, and Planetary Sciences from MIT where I worked at the intersection of climate science and scientific machine learning. As part of the Climate Modeling Alliance I developed Oceananigans.jl, a fast and flexible next-generation ocean model written in Julia that runs on GPUs, and used it to train machine learning models of geophysical turbulence and simulate all kinds of fluid dynamics.

And before that I received my MS and BS in Physics from the University of Waterloo where I used ultrashort pulse lasers and synchrotrons to make movies of molecular dynamics.

You can find out more about me and read my blog/ramblings on my personal website: http://aliramadhan.me/