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 (and established as a US domestic non-profit on June 14, 2021), 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

Affiliated Personnel

We are grateful to benefit from the expertise of these affiliated personnel via our fiscal sponsor, Future Earth International.

Kameliya Petrova

Consulting Director: Projects & Partnerships

Olivia Mendivil Ramos

Cold Spring Harbor Laboratory

Consulting Director: Operations

Core Team

Reuters Graphics

Agriculture & Forestry Lead

Hari Prasanna Das

Pacific Northwest National Laboratory

Community Platforms

AAAS Science & Technology Policy Fellowship

Media Relations

Columbia University

Meareg Hailemariam

Dakar American University of Science and Technology

Stanford University

Community Manager

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

Geneviève Patterson

Andrew Slavin Ross

Community Events

Maria João Sousa

Programs Vice Chair;
Summer School

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

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
Jessica Fan
Natasha Jaques
John Kieffer
Kelly Kochanski
Lauren Kuntz
Alexandre Lacoste
Wei-Wei Lin
Felipe Oviedo
Kris Sankaran
Katherine Stapleton
Anna Waldman-Brown
Yumna Yusuf
Sharon Zhou

Press

Releases

2021-2022 Innovation Grants Winners (May 5, 2022)
Climate Change and AI: Recommendations for Government (Nov. 8, 2021)
CCAI Innovation Grants program (Aug. 30, 2021)
MIT Technology Review 35 Under 35 awards (Jun. 30, 2021)
Paper: Tackling Climate Change with Machine Learning (Nov. 11, 2019)
- 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)