VIViralBShah·Dec 16, 2019
I am happy to share my perspective on this. I feel a really good blog on this topic is Bret Victor's blog post - What can a technologist do about climate change?
http://worrydream.com/ClimateChange/
One of the clear recommendations is - contribute to Julia. A lot of what Bret said in 2015 has actually panned out. Julia has become a powerful language for scientific computing and machine learning. As a result it is being used in climate projects such as Climate Machine (MIT and Caltech). The Julia Lab at MIT participates in this project:
Climate Machine: https://clima.caltech.edu/
Github: https://github.com/climate-machine/
Julia Lab: http://julia.mit.edu/
Contributions to Julia packages (compiler, stdlibs, math packages, ML packages, parallel computing) will end up finding their way into climate research, because of the extensive reuse of
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code within the Julia ecosystem. Specifically capabilities such as Zygote.jl (https://github.com/FluxML/Zygote.jl) for differentiable programming have the potential to dramatically make it easy to apply ML techniques to scientific codebases. Compiler contributors are hard to come along, so all contributions to compiler technology are incredibly valuable. The DiffEqFlux.jl ecosystem in Julia is a good example of combining mechanistic models with ML (https://github.com/JuliaDiffEq/DiffEqFlux.jl). Hop on to the Julia slack channel or discourse to dig in deeper.
Another thing Bret Victor speaks about in his blog post is working with agencies such as ARPA-E on advanced projects. Julia Computing is participating in an ARPA-E project to bring these capabilities to many energy related simulation and development technologies. This press release gives a broad idea:
ARPA-E press release: https://www.energy.gov/articles/department-energy-announces-...
Funded projects: https://arpa-e.energy.gov/sites/default/files/documents/file...
Julia Computing press release: https://juliacomputing.com/communication/2019/12/09/arpa-e.h...