About Climate Change AI

Climate Change AI (CCAI) is an organization composed of volunteers from academia and industry who believe that tackling climate change requires concerted societal action, in which machine learning can play an impactful role. Since it was founded in June 2019, CCAI has led the creation of a global movement in climate change and machine learning, encompassing researchers, engineers, entrepreneurs, investors, policymakers, companies, and NGOs.

Our Mission

To catalyze impactful work at the intersection of climate change and machine learning.

Our Goals

Community: To build a community of diverse stakeholders.
Education: To guide impactful work through educational resources and programs.
Infrastructure: To fill gaps in essential infrastructure such as funding, tools, and datasets.
Discourse: To advance discourse and advise relevant players.

Our Guiding Principles

On climate change: Climate change is a pressing issue with major implications for societal well-being, particularly for the world’s most disadvantaged populations. Addressing climate change requires rapid, sustained, equitable, and scientifically informed efforts in both mitigation and adaptation, in conjunction with relevant stakeholders.
On machine learning: Machine learning is a powerful tool with wide applicability in many technological and societal applications (both positive and negative), and should be practiced in a manner consistent with its strengths, weaknesses, and limitations, as well as with climate change goals (considering both its applications and its emissions footprint).
On machine learning for climate change: Machine learning can play an impactful role in many broader strategies for reducing and responding to climate change. At the same time, machine learning is not a silver bullet, and should serve to supplement (rather than divert attention from) other impactful actions to address climate change.
On diversity, inclusion, and equity: Diversity, inclusion, and equity are central to the advancement of society in general, and moreover fundamental to progress in addressing climate change. Where possible, it is important that work in climate change and machine learning attempt to address the structural inequities that exist in today’s society.

People

Core Team

Carnegie Mellon

Konstantin Klemmer

University of Warwick

Mila, U. de Montréal

Nikola Milojevic-Dupont

MCC Berlin, TU Berlin

Evan D. Sherwin

Stanford University

Hari Prasanna Das

Pacific Northwest National Laboratory

Elemental Cognition

Jessica Fan

QuantumBlack, McKinsey

Meareg Hailemariam

Dakar American University of Science and Technology

Stanford University

John Kieffer

University of Pittsburgh

Raphaela Kotsch

University of Zurich, ZHAW

Gaiascope, Inc.

Mila, Polytechnique Montréal

Ankur Mahesh

LBNL, UC Berkeley

UCPH, UC Berkeley

Peetak P. Mitra

University of Massachusetts

Microsoft AI for Good, MIT

Geneviève Patterson

Andrew Slavin Ross

Harvard University

Maria João Sousa

Katherine Stapleton

World Bank & Oxford University

Isabelle Tingzon

Thinking Machines Data Science

Yumna Yusuf

Climate policy, COP26

Advisors

Mila, U. de Montréal

Jennifer Chayes

MCC Berlin, TU Berlin

Stanford University

Cornell University

Konrad P. Körding

University of Pennsylvania

Carnegie Mellon

Stanford University

Organizational Structure

CCAI Chairs

Priya L. Donti
Lynn H. Kaack
David Rolnick

Content Committee

Sasha Luccioni (Committee Co-chair)
Nikola Milojevic-Dupont (Committee Co-chair)
Lauren Kuntz (Course)
Tegan Maharaj (Peer Review and Publishing)
Ankur Mahesh (Tutorials)
Geneviève Patterson (Course)
Isabelle Tingzon (Tutorials)
Marcus Voss (Wiki)

Communications Committee

Konstantin Klemmer (Committee Chair)
Ján Drgoňa (Forum)
Jesse Dunietz (Media Relations)
Peetak Mitra (Newsletter)
Andrew Ross (Website)
Katherine Stapleton (Strategic Outreach)
Kasia Tokarska

Programs Committee

Evan D. Sherwin (Committee Chair)
Hari Prasanna Das (Summer School)
Jessica Fan (Industry Events)
Meareg Hailemariam (Webinar)
Jeremy Irvin (Summer School)
John Kieffer (Community Events)
Konstantin Klemmer
Felipe Oviedo (Webinar)
Maria João Sousa (Summer School)
Yumna Yusuf (Happy Hours)

Community Leads

David Dao (Agriculture, Forestry, and Other Land Use)
Priya L. Donti (Power Sector)
Lynn H. Kaack (Public Sector)
Raphaela Kotsch (Economics and Markets)
Nikola Milojevic-Dupont (Buildings and Transportation)
Kelton Minor (Computational Social Sciences)
David Rolnick (Tech Industry and ML Academia)
Katherine Stapleton (International Organizations)
Kasia Tokarska (Climate and Earth Sciences)

Former Core Team Members

Ebude Antem Yolande Ebong
Natasha Jaques
Kelly Kochanski
Alexandre Lacoste
Wei-Wei Lin
Kris Sankaran
Anna Waldman-Brown
Sharon Zhou

Press

Releases

Nov. 11, 2019: press release and press packet

Selected coverage

“Not Cool: A Climate Podcast” by the Future of Life Institute: Part 1 and Part 2
Eye on A.I. podcast: “Climate Change and AI”
National Geographic: “How artificial intelligence can tackle climate change”
The Verge: “Here’s how AI can help fight climate change according to the field’s top thinkers”
MIT Technology Review: “Here are 10 ways AI could help fight climate change”