Venkatesh

PhD student at @Mila_Quebec, working on the Machine Learning for climate science and biodiversity conservation.

[Julia Kaltenborn @JuliaKaltenborn]

The ML community has been calling for a large scale climate dataset. In our recent Neurips 2023 publication, we introduce ClimateSet: A
➡️ large-scale
➡️ consistent
➡️ ML-accessible
climate model dataset 🌍. 1/8

https://t.co/lkXBYeUGun

https://t.co/GyNWO7Cm3x

[Qidong Yang @qidong_yang]

High-res climate simulations are essential but computationally expensive. In this work, we present an FNO-based method to downscale climate data to arbitrarily high resolution.(https://t.co/2jxPdIY8M3) Our method beats state-of-the-art even on resolutions it wasn't trained on. 🧵

[paulaharder.bsky.social @PaulaHarder8]

Working in ML for downscaling? Overwhelmed by the amount of literature? Some friends and me starting collecting all the papers out there: https://t.co/k56BVCikKw
@JuliaKaltenborn @jogonzab @venka_r @alexhdezgcia #downscaling #superresolution #aiforclimate

[paulaharder.bsky.social @PaulaHarder8]

High-resolution climate data is very useful but hard to get. Deep learning downscaling methods can help, but how can you ensure that the produced outcomes are physically consistent? 1/ https://t.co/PkukSmEgsr

[paulaharder.bsky.social @PaulaHarder8]

Presented our work on Physics-Constrained Deep Learning for Climate Super-Resolution at the @RealAAAI Fall symposium: https://t.co/oWsk18zuSJ.
Thanks a lot to all the organizers and attendees for such an amazing event and inspiring discussions! @ClimateChangeAI

[David Rolnick @david_rolnick]

Excited to announce our new paper & dataset on approximating atmospheric radiative transfer using ML (#NeurIPS2021 Datasets & Benchmarks track) led by @CachaySalva & @venka_r in collab with @environmentca. Presentation today at 8:30 am PST, full paper at https://t.co/gwVH9M1NoF

[christina @cszhu]

Me: *has a computer science degree, has been programming for a few years*

Also me: *googles 'how to declare a list in Python'*