[Grad-postdoc-assn] AI for Earth Sciences at ICLR 2020 in Africa - Due Feb 14th

[S. Karthik Mukkavilli]

Dear All

Just a gentle reminder that submissions to the AI for Earth Sciences
workshop <https://ai4earthscience.github.io/iclr-2020-workshop/> happening
at ICLR 2020 <https://iclr.cc/>, a global AI research conference happening
in Africa for the first time, are due soon. Please share with your
collaborators.

*7 Feb 2020* - 3-6 page Full Paper Deadline
*14 Feb 2020* - Abstract-Only Deadline (informal AGU/EGU style)

Here is a link to the submission on Microsoft CMT
<https://cmt3.research.microsoft.com/AI4ESICLR2020>. Deadlines are at 11:59
PST (California time) of date listed.

Thank you
Karthik
PS: We currently also seek sponsors to help organise this workshop in Addis
Ababa, Ethiopia.

--
AI for Earth Sciences

Earth sciences or geosciences encompasses understanding the physical
characteristics of our planet, including its lithosphere, hydrosphere,
atmosphere and biosphere, applying all fields of natural and computational
sciences. As Earth sciences enters the era of increasing volumes and
variety of geo-scientific data from sensors, as well as high performance
computing simulations, machine learning methods are poised to augment and
in some cases replace traditional methods. Interest in the application of
machine learning, deep learning, reinforcement learning, computer vision
and robotics to the geosciences is growing rapidly at major Earth science
and machine learning conferences.

Our workshop 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, geo-health (i.e. water, land and air pollution), volcanism,
seismology and biogeosciences.
Topics of Interest

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 and
reinforcement learning. New labelled Earth science datasets and
visualizations with machine learning is also of particular interest.
Submission Instructions

There are two tracks for workshop submission:

1) Full Paper submission: 3-6 pages excluding references and supplementary
materials. Papers are encouraged to use ICLR Format
<https://github.com/ICLR/Master-Template/blob/master/archive/iclr2020.zip>.
We also welcome dataset labeling papers submitted as a 3 page proposal as
described in AI4Earth
<https://ai4edevshare.blob.core.windows.net/ai4emisc/Proposal%20Requirements.pdf>

2) Abstract-Only submission: 300 word limit (AGU/EGU style abstract)

Submissions may be made on CMT
<https://cmt3.research.microsoft.com/AI4ESICLR2020>. ICLR workshop
registration is necessary for attendance, but one need not attend the
entire conference.
Important Dates

*21 Jan 2020* - ICLR Registration opens
*7 Feb 2020* - Full Paper Deadline
*14 Feb 2020* - Abstract-Only Deadline
*25 Feb 2020* - Acceptance Notifications
*26 April 2020* - Workshop Date

Deadlines are at 11:59 PST (California time) of date listed.
Schedule

This full day workshop will include keynotes, invited talks, regular talks,
spotlight talks, and a panel discussion with a mix of keynote speakers and
organisers with audience Q/A. Posters will be available throughout the day
and in a dedicated viewing session.
Organizing Committee

S. Karthik Mukkavilli <https://mila.quebec/en/person/karthik-mukkavilli/>,
Postdoc at Mila
Aaron Courville <https://mila.quebec/en/person/aaron-courville/>, Associate
Professor at Mila and Université de Montréal
Kelly Kochanski <https://www.kochanski.org/kelly/>, PhD Candidate at CU
Boulder
Johanna Hansen <https://johannah.github.io/>, PhD Candidate at McGill
University
Steering Committee

Vipin Kumar <https://www-users.cs.umn.edu/~kumar001/>, Chaired Professor at
Minnesota in Computer Science and Engineering
Gregory Dudek <http://www.cim.mcgill.ca/~dudek/>, Chaired Professor at
McGill School of Computer Science
Pierre Gentine <https://eee.columbia.edu/faculty/pierre-gentine>, Associate
Professor of Earth and Environmental Engineering, Columbia University
Mary C Hill <https://geo.ku.edu/hill-mary-c>, Professor of Geology at
University of Kansas
Trooper Sanders <https://twitter.com/troopersanders?lang=en>, CEO at
Benefits Data Trust
Chad Frischmann <https://www.drawdown.org/staff/chad-frischmann>, VP &
Research Director at Drawdown
Paul D. Miller, aka DJ Spooky <http://djspooky.com/>
Program Committee

Auroop Ganguly (Northeastern)
Philippe Tissot (Texas A & M)
Amy McGovern (University of Oklahoma)
David Gagne (NCAR)
Ashley Pilipiszyn (Stanford and OpenAI)
David Meger (McGill)
Karthik Kashinath (Berkeley Lab)
Christiane Jablonowski (University of Michigan)
Daniel Fuka (Virginia Tech)
Julien Brajard (NERSC/Sorbonne University)
Udit Bhatia (IIT Gandhinagar)
Redouane Lguensat (CNES/Universite Grenoble Alpes)
Victor Schmidt (Mila)
Tom Beucler (UC Irvine)
Aven-Satre Meloy (Oxford)
Agnieszka Słowik (Cambridge)
Contact Us

Send inquiries to ai4earthscience[at]gmail[dot]com
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