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

Board of Directors

Carnegie Mellon

CCAI Chair;
Power & Energy Lead

CCAI Chair;
Public Sector Lead

CCAI Chair;
Biodiversity Lead

Konstantin Klemmer

University of Warwick

Communications Chair

Raphaela Kotsch

University of Zurich, ZHAW

Community Leads Chair;
Economics & Markets Lead

Nikola Milojevic-Dupont

MCC Berlin, TU Berlin

Content Chair;
Buildings & Transportation Lead

Evan D. Sherwin

Stanford University

Personnel

Geneviève Patterson

Director of Projects & Partnerships;
Course

Olivia Mendivil Ramos

Cold Spring Harbor Laboratory

Director of Operations

Core Team

Reuters Graphics

Agriculture & Forestry Lead

Hari Prasanna Das

Pacific Northwest National Laboratory

Community Platforms

Elemental Cognition

Media Relations

Jessica Fan

QuantumBlack, McKinsey

Industry Events

Columbia University

Meareg Hailemariam

Dakar American University of Science and Technology

Stanford University

Community Manager

Gaiascope, Inc.

Mila, U. de Montréal

Mila, Polytechnique Montréal

Peer Review & Publishing

Ankur Mahesh

LBNL, UC Berkeley

Tutorials

UCPH, UC Berkeley

Computational Social Sciences Lead

Peetak P. Mitra

Palo Alto Research Center

Microsoft AI for Good, MIT

Andrew Slavin Ross

Oregon State University

Community Events

Maria João Sousa

Programs Vice Chair;
Summer School

Katherine Stapleton

World Bank, Oxford University

International Organizations Lead

Isabelle Tingzon

Thinking Machines Data Science

Community Leads Vice Chair;
Climate & Earth Sciences Lead

Birds on Mars, TU Berlin

Kureha Yamaguchi

University of Cambridge

Community Manager

Yumna Yusuf

Climate policy, COP26

Happy Hours

Advisory Board

Stanford University

Mila, U. de Montréal

Jennifer Chayes

MCC Berlin, TU Berlin

Cornell University

Carnegie Mellon

Konrad P. Körding

University of Pennsylvania

Claire Monteleoni

Catherine Nakalembe

University of Maryland

Stanford University

Former Core Team Members

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

Press

Releases

Climate Change and AI: Recommendations for Government (Nov. 8, 2021): press release
CCAI Innovation Grants program (Aug. 30, 2021): press release
MIT Technology Review 35 Under 35 awards (Jun. 30, 2021): press release
Paper: Tackling Climate Change with Machine Learning (Nov. 11, 2019): press release and press packet

Selected articles

The Economist Intelligence Unit: Green Intelligence - AI could boost efforts to fight climate change (May 2021)
Capgemini: Climate AI: How artificial intelligence can power your climate action strategy (Nov 2020)
Forbes: Is Fusion Really Close To Reality? Yes, Thanks To Machine Learning (Apr 2020)
CleanTechnica: Machine Learning Experts Issue Call To Arms For Climate Focus (Jan 2020)
National Geographic: How artificial intelligence can tackle climate change (Jul 2019)
The Verge: Here’s how AI can help fight climate change according to the field’s top thinkers (Jun 2019)
MIT Technology Review: Here are 10 ways AI could help fight climate change (Jun 2019)

Selected podcasts and radio shows

The Interchange: How A.I. Will Revolutionize Climate Tech (Jun 2021)
Körber Stiftung: Der Zusammenhang von Klima und KI (Mar 2021, German)
ASP Flashpoint: Climate and AI (Aug 2020)
Deutschlandfunk: Künstliche Intelligenz gegen den Klimawandel (May 2020, German)
The Interchange: Beyond Forecasting: Artificial Intelligence Is a Powerful Decarbonization Tool (Feb 2020)
“Not Cool: A Climate Podcast” by the Future of Life Institute: Part 1 and Part 2 (Oct 2019)
Eye on A.I. podcast: Climate Change and AI (Sep 2019)