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

Lynn H. Kaack
Hertie School
Co-Founder
CCAI Chair
Public Sector Co-Lead

David Rolnick
McGill, Mila
Co-Founder
CCAI Chair

Maria João Sousa
IST, ULisboa, Cornell Tech
CCAI Chair
Incoming Executive Director
Summer School

Konstantin Klemmer
Microsoft Research
Communications Chair

Olivia Mendivil Ramos
OneThree Biotech
Content Chair

Marcus Voss
Birds on Mars, TU Berlin
Community Leads Chair
Buildings & Transportation Lead
Staff

Priya L. Donti
MIT
Co-Founder
Executive Director

Kameliya Petrova
Consulting Director: Projects & Partnerships
via Future Earth International
via Future Earth International

Erick Kapp
Educational Events Coordinator

Xiaojuan Liu
Research Scientist (Data Gaps)

Eric Scheier
Research Scientist (Data Gaps)
Core Team

Annie Agle
Cambridge Institute for Sustainable Leadership and Cotopaxi
Industry Events

Utkarsha Agwan
UC Berkeley
Community Events

Nadia Ahmed
UC Irvine
Tutorials

Olalekan Akintande
University of Ibadan
Community Events

Sara Beery
MIT, Google
Biodiversity Lead

Rasika Bhalerao
Northeastern University
Communications Vice Chair
Newsletter

Millie Chapman
UC Berkeley
Summer School

Ioana Colfescu
National Centre for Atmospheric Science
Course

David Dao
ETH Zürich
Agriculture & Forestry Lead

Hari Prasanna Das
UC Berkeley
Summer School
Jesse Dunietz
AAAS Science & Technology Policy Fellowship
Media Relations

Simone Fobi
Microsoft AI for Good Research Lab
Data

Alan Fortuny
UNED Adidas
Newsletter
Blog

Jade Eva Guisiano
ISEP, Polytechnique, UNEP
Course

Meareg Hailemariam
Dakar American University of Science and Technology
Data

Melanie Hanna
DataRobot
Tutorials

Jeremy Irvin
Stanford University
Summer School
Course

Kai Jeggle
ETH Zürich
Volunteer Matching

Nathan Kiner
Sunjul
Course

Samuel King
Briink GmbH
Community Events

Raphaela Kotsch
University of Zurich, ZHAW
Economics & Markets Lead

Alp Kucukelbir
Fero Labs, Columbia University
Entrepreneurship Lead

Sasha Luccioni
Hugging Face

Shiva Madadkhani
TU München
Community Events

Nikola Milojevic-Dupont
MCC Berlin, TU Berlin
Content Vice Chair

Jorge Montalvo
Centrica
Community Events

Panayiotis Moutis
City College of New York
Power & Energy Lead

Arthur Ouaknine
McGill University, Mila
Webinars

Amanda Sessim Parisenti
Summer School

Shafat Rahman
Otoll
Tutorials

Andrew Slavin Ross
Arcadia
Website

Mark Roth
Climate, LLC
Programs Vice Chair
Community Events

Sebastian Ruf
Northeastern University
Blog

Evan D. Sherwin
Stanford University
Programs Chair

Daniel Spokoyny
Carnegie Mellon
Summer School

Isabelle Tingzon
Thinking Machines Data Science
Tutorials

Kasia Tokarska
ETH Zürich
Community Leads Vice Chair
Climate & Earth Sciences Lead

Marius Wiggert
UC Berkeley
Community Events

Gina Wong
Johns Hopkins
Data

Kureha Yamaguchi
University of Cambridge
Community Platform Lead
Advisory Board

Inês Azevedo
Stanford University

Yoshua Bengio
Mila, U. de Montréal

Jennifer Chayes
UC Berkeley

Felix Creutzig
MCC Berlin, TU Berlin

Carla Gomes
Cornell University

Demis Hassabis
DeepMind

Zico Kolter
Carnegie Mellon

Konrad P. Körding
University of Pennsylvania

Claire Monteleoni
CU Boulder

Catherine Nakalembe
University of Maryland

Andrew Y. Ng
Stanford University

John C. Platt
Google AI

Tobias Schmidt
ETH Zürich

Craig Smith
Eye on AI
Former Core Team Members
- Dea Bankova
- Zikri Bayraktar
- Ashesh Chattopadhyay
- Ján Drgoňa
- Ebude Antem Yolande Ebong
- Jessica Fan
- Soledad Galli
- Natasha Jaques
- John Kieffer
- Kelly Kochanski
- Lukas Kondmann
- Lauren Kuntz
- Alexandre Lacoste
- Wei-Wei Lin
- Tegan Maharaj
- Ankur Mahesh
- Kelton Minor
- Peetak P. Mitra
- Felipe Oviedo
- Geneviève Patterson
- Kris Sankaran
- Katherine Stapleton
- Anna Waldman-Brown
- Sharon Xu
- Yumna Yusuf
- Sharon Zhou
Press
Releases
- CCAI Innovation Grants 2023 (Nov. 4, 2022)
- 2021-2022 Innovation Grants Winners (May 5, 2022)
- Climate Change and AI: Recommendations for Government (Nov. 8, 2021)
- CCAI Innovation Grants 2022 (Aug. 30, 2021)
- MIT Technology Review 35 Under 35 awards (Jun. 30, 2021)
- Paper: Tackling Climate Change with Machine Learning (Nov. 11, 2019)
Selected articles
- Popular Science: AI can help fight climate change—but it can also make it worse (May 2022)
- Der Spiegel: How High-Tech Tools Are Helping Combat Climate Change (Sep 2021)
- pv magazine: Climate Change AI unveils US$2 million grant program (Aug 2021)
- 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
- WiDS Podcast: Priya Donti | Using AI to Fight the Climate Crisis (Jan 2023)
- 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)