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AI for Earth Sciences
Surya Karthik Mukkavilli · Johanna Hansen · Natasha Dudek · Tom Beucler · Kelly Kochanski · Mayur Mudigonda · Karthik Kashinath · Amy McGovern · Paul D Miller · Chad Frischmann · Pierre Gentine · Gregory Dudek · Aaron Courville · Daniel Kammen · Vipin Kumar

Sat Dec 12 06:45 AM -- 09:00 PM (PST) @ None
Event URL: https://ai4earthscience.github.io/neurips-2020-workshop/ »

Our workshop proposal AI for Earth sciences seeks to bring cutting edge geoscientific and planetary challenges to the fore for the machine learning and deep learning communities. We seek machine learning interest from major areas encompassed by Earth sciences which include, atmospheric physics, hydrologic sciences, cryosphere science, oceanography, geology, planetary sciences, space weather, volcanism, seismology, geo-health (i.e. water, land, air pollution, environmental epidemics), biosphere, and biogeosciences. We also seek interest in AI applied to energy for renewable energy meteorology, thermodynamics and heat transfer problems. We call for papers demonstrating novel machine learning techniques in remote sensing for meteorology and geosciences, generative Earth system modeling, and transfer learning from geophysics and numerical simulations and uncertainty in Earth science learning representations. We also seek theoretical developments in interpretable machine learning in meteorology and geoscientific models, hybrid models with Earth science knowledge guided machine learning, representation learning from graphs and manifolds in spatiotemporal models and dimensionality reduction in Earth sciences. In addition, we seek Earth science applications from vision, robotics, multi-agent systems and reinforcement learning. New labelled benchmark datasets and generative visualizations of the Earth are also of particular interest. A new area of interest is in integrated assessment models and human-centered AI for Earth.

AI4Earth Areas of Interest:
- Atmospheric Science
- Hydro and Cryospheres
- Solid Earth
- Theoretical Advances
- Remote Sensing
- Energy in the Earth system
- Extreme weather & climate
- Geo-health
- Biosphere & Biogeosciences
- Planetary sciences
- Benchmark datasets
- People-Earth

Author Information

Surya Karthik Mukkavilli (University of California, Irvine, Berkeley Lab & McGill)
Johanna Hansen (McGill University)
Natasha Dudek (McGill-Mila)
Tom Beucler (University of California, Irvine)
Kelly Kochanski (University of Colorado Boulder)

Earth science researcher using machine learning to make better predictions about natural hazards and climate change.

Mayur Mudigonda (UC Berkeley)
Karthik Kashinath (LBNL)
Amy McGovern (University of Oklahoma)
Paul D Miller (DJ Spooky)
Chad Frischmann (Drawdown)
Pierre Gentine (Columbia University)
Gregory Dudek (McGill University & Samsung Research)
Aaron Courville (U. Montreal)
Daniel Kammen (University of California, Berkeley)
Vipin Kumar (University of Minnesota)

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