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Climate Change AI will be holding a five-day virtual workshop on climate change and machine learning from April 26-30, as part of the machine learning conference ICLR. The main workshop on April 26 will feature a full-day program of invited and contributed presentations, and sessions between April 27-30 will feature deep dives into specific sectors as well as ample opportunities to engage with other experts.

To successfully use machine learning in climate change problems, it’s important to have an interdisciplinary lens. We have prepared a program designed to be worthwhile both for researchers in machine learning as well as experts in areas related to climate change mitigation and adaptation.  We invite you to share this message widely to anyone who may be interested in attending.

We’re excited to bring the community together for this workshop, and hope to see you there!
Main Workshop (April 26)

The virtual format will consist of a series of streamed talks and panels, along with a poster session taking place on Zoom. By registering in advance, you’ll be able to ask questions of our speakers and join live discussions with other participants. (Non-registered participants can still access the livestream on our website). Registration is $100 USD ($50 for students), and can be found here. Calendars with the full schedule are available in GCal and iCal.

For the main workshop on April 26, our invited keynotes are
  • Ciira wa Maina [Dedan Kimathi University of Technology], whose work in bioacoustics, IoT, and machine learning addresses challenges in conservation.
  • Georgina Campbell Flatter [ClimaCell], whose work seeks to improve the availability of weather information in regions where it is limited, in order to guide climate resilience.
  • Stefano Ermon [Stanford University], who will describe how automated analysis of satellite imagery can provide novel data on infrastructure and agricultural productivity, to inform adaptation at fine spatial and temporal resolution.
  • Dan Morris [Microsoft AI for Earth], who will describe the relationship between climate, biodiversity, and land use, emphasizing the way machine learning can directly support conservation scientists.
We’re also excited to have panelists with experience across climate change mitigation and adaptation: John Platt (Google), Nana Ama Browne Klutse (University of Ghana), Dan Kammen (UC Berkeley), Jessica Thorn (University of York), Sarvapali Ramchurn (University of Southampton), and Paula Hidalgo-Sanchis (UN Global Pulse).

A list of accepted papers can be found here -- authors will be presenting during the two poster sessions on the 26th. Selected posters will also be featured in spotlight talks.
Virtual Booths (April 27-30)
Want to know how the global pandemic might change the course of climate action? Are you interested in strengthening your foundation in climate change or machine learning concepts? Then you might be interested in attending one of our four informal, thematic sessions, taking place as a part of ICLR’s virtual booths. Each session will be a mix of short invited talks, tutorials, and moderated discussion.
  • April 27: Tune into Energy Day for a discussion of machine learning and the energy transition, with topics including optimizing the electricity grid,  decarbonizing cities, and the long-term impacts of COVID-19.
  • April 28: On Agriculture, Forestry, and Other Land Use (AFOLU) Day, you’ll learn about how data could be used to make agriculture carbon neutral and how drones and satellite images can support reforestation efforts, among other topics.
  • April 29: Climate Science and Adaptation Day will explore the cutting edge of climate science, including how artificial intelligence can dramatically accelerate Global Climate Models, and will describe the potential for localized, data-driven climate change adaptation.
  • April 30: Cross-cutting Methods Day will distill unifying themes, inviting speakers and participants to share their perspectives on the key methodological and conceptual challenges lying at the boundary of climate change and machine learning.
Detailed Schedule

Main Workshop (April 26)
Time (UTC) Event
8:45 - 9:00 Welcome and opening remarks
9:00 - 10:15 Panel: Ciira wa Maina, Georgina Campbell Flatter, Sarvapali Ramchurn, Paula Hidalgo-Sanchis
10:15 - 11:00 Invited talk: Ciira wa Maina
11:00 - 12:00 Spotlight talks
12:00 - 13:00 Poster session
13:00 - 13:45 Georgina Campbell Flatter: Why the Climate Change AI Community Should Care About Weather: A New Approach for Africa (Invited talk)
13:45 - 15:30 Break and small-group discussions
15:30 - 17:00 Panel: Dan Kammen, Dan Morris, Jessica Thorn, John Platt, Nana Ama Browne Klutse, Stefano Ermon
17:00 - 17:45 Stefano Ermon: Measuring Economic Development from Space with Machine Learning (Invited talk)
17:45 - 18:45 Spotlight talks
18:45 - 20:00 Poster session
20:00 - 20:45 Dan Morris: Climate, biodiversity, and land: using ML to protect and restore ecosystems (Invited talk)
20:45 - 21:00 Closing remarks and conclusion

The Virtual Booth days will have sessions on, among other topics,
  • Tutorial: Machine Learning 101 for Climate Change
  • Tutorial: Climate Change 101 for Machine Learning
  • Opportunities and Challenges for Machine Learning in the African Electricity Sector
  • Implications of COVID-19 for the Energy Transition and Machine Learning
  • Emulating Physical Models
  • Accelerated science and experimentation
  • Remote sensing
  • Forecasting
See the workshop website for more information on additional sessions.
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Dates and deadlines reported in this newsletter may change, and CCAI is not responsible for any inadvertent inaccuracies.

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