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Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Appendix C: Workshop Agenda

Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges—A Workshop

February 7, 10, and 11, 2022 | 1:00-4:00 pm (All times EST)

Workshop Goal: This workshop will consider the opportunities and challenges of using machine learning and artificial intelligence (ML/AI) to advance Earth system science, including their ethical development and use. The workshop will convene Earth system science experts, ML and AI researchers, social and behavioral scientists, ethicists, and decision makers across sectors to explore how these approaches can contribute to improving understanding, analysis, modeling, prediction, and decision making. Workshop discussions will examine state-of-the-art approaches for using ML/AI for Earth system science, consider challenges and ways to mitigate risks of using ML/AI, and identify future opportunities to accelerate progress.

1:00 pm Welcome and Opening Remarks
Ruby Leung, Planning Committee Chair, Pacific Northwest National Laboratory

Session 1: Overview of State-of-the-Art Use of ML/AI for Earth System Science
Moderators: Amy McGovern, University of Oklahoma, and Ruby Leung, PNNL

Session 1 will provide a broad overview of how ML/AI are currently being used for Earth system science, remaining conceptual and technical challenges, and opportunities to address those challenges.

1:10 pm 20-minute talk followed by discussion
Peter Dueben, European Centre for Medium-Range Weather Forecasts
1:40 pm Break

Session 2: Emerging Approaches for Using and Interpreting ML/AI
Moderators: Patrick Heimbach, UT Austin, and Aarti Singh, CMU

Session 2 will consider unique challenges in Earth system science that emerging ML/AI approaches can help to address. Specific approaches will include integrating physics, expert knowledge, multiple modalities of data, and ML/AI techniques; explainable AI and interpretable ML; and data assimilation.

1:45 pm 12-minute talks followed by discussion
  • Pierre Gentine, Columbia University
Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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  • Elizabeth Barnes, Colorado State University
  • Stephen Penny, Sofar Ocean
2:45 pm Break

Session 3: Emerging Opportunities from Social and Human Engineered Systems
Moderators: Ann Bostrom, UW, and Ruby Leung, PNNL

Session 3 panelists will discuss opportunities for using ML/AI to understand social and human engineered systems, and the prospects for using ML/AI to integrate social and human engineered system science into Earth system science.

3:00 pm 5-minute panelist remarks followed by discussion
  • Auroop Ganguly, Northeastern University
  • Abigail Snyder, Pacific Northwest National Laboratory
  • David Rolnick, McGill University
  • Jennifer Chayes, UC Berkeley
3:50 pm Closing Remarks and Plans for Day 2
Amy McGovern, University of Oklahoma
4:00 pm Adjourn
1:00 pm Welcome and Opening Remarks
Ruby Leung, Committee Chair, Pacific Northwest National Laboratory
1:10 pm Recap of Workshop Day 1
Patrick Heimbach, UT Austin, and Diego Melgar, U of Oregon

Session 1: Responsible and Ethical Use and JEDI Issues for ML/AI in Weather, Climate, and Earth System Science
Moderators: Amy McGovern, University of Oklahoma, and Ann Bostrom, UW

Session 1 panelists will consider what ethical standards should guide Earth system science, and how such standards relate to the lenses of justice, equity, diversity, and inclusion (JEDI). Panelists will also discuss potential biases in ML/AI for Earth system science and promising ways to avoid those biases.

1:20 pm 5-minute panelist remarks followed by discussion
  • David Danks, UC San Diego
  • Imme Ebert-Uphoff, Colorado State University
  • Priya Donti, Carnegie Mellon University
  • Abhishek Gupta, Montreal AI Ethics Institute
Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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2:10 pm Break

Session 2: Workforce Development Capacity and Skill Sets
Moderators: Diego Melgar, U of Oregon, and Amy McGovern, University of Oklahoma

Session 2 conversationalists will discuss gaps in education for those working at the intersection of ML/AI and Earth system science, needs and strategies for the private sector and academia in workforce development, the role of continued education in the current workforce, and capacity building at earlier educational stages.

2:20 pm Brief introductions followed by conversation
  • Lak Lakshmanan, Google
  • Hamed Alemohammad, Radiant Earth
  • Terri Adams, Howard University
  • Rebecca Nugent, Carnegie Mellon University

Session 3: Challenges and Opportunities for Earth Science Technology and Data
Moderators: Patrick Heimbach, UT Austin, and Laure Zanna, NYU

Session 3 panelists will consider open data, standards, and platforms to facilitate open science for ML/AI and Earth system science as well as technology development, funding models, and education challenges and opportunities for Earth system science technology and data.

3:00 pm 5-minute panelist remarks followed by discussion
  • Ryan Abernathey, Columbia University
  • Chelle Gentemann, Farallon Institute
  • Jason Hickey, Google
  • Ana Privette, Amazon Sustainability Data Initiative
  • Katie Dagon, National Center for Atmospheric Research
3:50 pm Closing Remarks and Plans for Day 3
Laure Zanna, NYU
4:00 pm Adjourn
1:00 pm Welcome and Opening Remarks
Ruby Leung, Committee Chair, Pacific Northwest National Laboratory
1:10 pm Recap of Workshop Day 2
Ruby Leung, PNNL, and Ann Bostrom, UW
Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

Session 1: Using ML/AI for Data-Driven Decision Making
Moderators: Ann Bostrom, UW, and Diego Melgar, University of Oregon

Session 1 speakers will discuss the role of ML/AI at the interface of predictive physical models and real-time decision making, handling uncertainties, and the remaining scientific, engineering, societal, and ethical challenges in this space.

1:20 pm 12-minute talks followed by discussion
  • Elizabeth Cochran, U.S. Geological Survey
  • Pierre Lermusiaux, MIT
  • Daniel Rothenberg, Waymo
2:20 pm Break

Session 2: Novel Funding Mechanisms, Partnerships, and Knowledge Transfer Between Academia, Industry, Nonprofits, and Government
Moderators: Aarti Singh, CMU, and Laure Zanna, NYU

Session 2 conversationalists will consider funding opportunities, effective mechanisms, and creative new approaches to facilitate partnerships and knowledge transfer among academia, industry, nonprofits, and government to advance ML/AI for Earth system science.

2:40 pm Brief introductions followed by conversation
  • Lynne Parker, White House Office of Science and Technology Policy
  • Gary Hattem, Independent Advisor
  • Jennifer Chayes, UC Berkeley
  • Qingkai Kong, Lawrence Livermore National Laboratory
  • David Spergel, Simons Foundation
3:40 pm Summary of Workshop Day 3
Aarti Singh, CMU, and Amy McGovern, University of Oklahoma
3:50 pm Closing Remarks
Ruby Leung, Committee Chair, Pacific Northwest National Laboratory
4:00 pm Adjourn
Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
Page 55
Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
Page 56
Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
Page 57
Suggested Citation:"Appendix C: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
Page 58
Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop Get This Book
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 Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop
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The Earth system - the atmospheric, hydrologic, geologic, and biologic cycles that circulate energy, water, nutrients, and other trace substances - is a large, complex, multiscale system in space and time that involves human and natural system interactions. Machine learning (ML) and artificial intelligence (AI) offer opportunities to understand and predict this system. Researchers are actively exploring ways to use ML/AI approaches to advance scientific discovery, speed computation, and link scientific communities.

To address the challenges and opportunities around using ML/AI to advance Earth system science, the National Academies convened a workshop in February 2022 that brought together Earth system experts, ML/AI researchers, social and behavioral scientists, ethicists, and decision makers to discuss approaches to improving understanding, analysis, modeling, and prediction. Participants also explored educational pathways, responsible and ethical use of these technologies, and opportunities to foster partnerships and knowledge exchange. This publication summarizes the workshop discussions and themes that emerged throughout the meeting.

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