Climate Change AI Virtual Summer School 2026

We are excited to host the fourth Climate Change AI Virtual Summer School in 2026! This year’s program will take place from July 20–August 22, 2026.

The CCAI Virtual Summer School provides participants with the opportunity to learn about different applications of artificial intelligence (AI) for climate action from expert researchers and practitioners, and to develop important skills and expertise to make an impact in this space. Through lectures and hands-on tutorials, participants will

The program is designed for a global audience from across sectors and career stages, including researchers, practitioners, decision-makers, and students across academia, industry, entrepreneurship, civil society, and the public sector. The program content is particularly geared towards participants who have some prior engagement with either AI or climate-relevant areas and want to learn more about their intersection. If you are interested in learning about ways to responsibly and impactfully leverage AI to tackle major climate problems, we encourage you to check out the program!

Dates & Key Info

📅 Dates: July 20 – August 22, 2026

📍 Location: Virtual

🕒 Schedule: Lectures will be presented live, as well as recorded. Tutorials will be fully asynchronous. See the Schedule section of the website for the full schedule. Participants do not need to attend all lectures – they are welcome to engage with specific lectures and tutorials based on their interest and availability.

🏅Certificate of Participation: Certificates of participation will be provided to those who complete 6 foundation modules and at least 4 sectoral modules (see Program Structure). Live lecture attendance is not required to receive a certificate. Participants will have until September 20, 2026 to complete the requirements and receive a certificate. Participants will retain access to program content indefinitely after the program.

💬 Language: The program will be conducted in English. Lectures will include live interpretation into French, Italian, Spanish, and Portuguese. Live automatic subtitles in additional languages will also be available via Zoom.

🎟️ Registration fee: $30 (USD) for participants residing in high-income economies and $10 (USD) for participants residing outside high-income economies. Fee waivers are also available, as we are eager to ensure that program fees are not a barrier to participation for any individual.

Registration deadline: Registration will remain open until the end of the program. It is not too late to register! Participants who register after the program has begun will receive access to all previously released content, in addition to being able to participate in the remaining live sessions.

📧 Contact: summerschool+virtual@climatechange.ai. Please also see the Frequently Asked Questions section in case your question is already answered there.

Register

Registration for the program is open, and will remain open until the end of the program! You can register either via standard registration or a fee waiver registration. Please be sure to fill out the appropriate form based on the type of registration you are requesting. A group registration option is also available.

📝 Standard Registration: You can register for the program by filling out the following form: https://forms.gle/uT31HMKBjRE9thot7

🆓 Fee Waiver Registration: To apply for a fee waiver (i.e., free registration), please fill out the fee waiver registration form: https://forms.gle/PDStfmJPR9655iBv9

👤👤 Group Registration: Groups may pay in bulk for multiple registrations via the following link: https://buy.stripe.com/14A4gzcri9X2dPh1oQ6Ng24

Program Structure

Throughout the program in July and August, participants will engage in various “content modules” – each consisting of pre-readings, a lecture, and often a hands-on coding tutorial – that aim to equip participants with an overview of a given area and provide hands-on practice with a particular application and/or tool. Content modules come in two forms:

In addition, self-study materials will be made available to help participants without prior background in AI or coding acquire relevant background necessary for completion of the hands-on coding tutorials. (These materials are optional, i.e., are not required for participation in the Virtual Summer School program.)

Certificate of participation requirements: To receive a certificate of participation, you must complete 6 foundation modules and at least 4 sectoral modules. Participants will have until September 20, 2026 to complete all certificate requirements. Live lecture attendance is not required to receive a certificate; lectures will both be presented live and also be recorded. Tutorials (interactive Python coding notebooks) will be fully asynchronous.

Content access: Participants will retain access to the program’s lectures and tutorials indefinitely after the program.

Program platform: All program participants will be onboarded to the Climate Change AI Community Platform, which will be used for all program-related communications. The Community Platform is an online forum where they can engage in discussions with others, participate in Q&A on lectures and tutorials, and engage in discussions on climate change and AI projects and topics with the broader Climate Change AI community. Virtual Summer School participants will retain access to the Community Platform even after the end of the program. In addition, participants will be onboarded to a platform called LearnWorlds to gain access to program materials.

Instructors

Lecturers

Tẹjúmádé Àfọ̀njá
PhD Candidate, CISPA Helmholtz Center | Co-founder, TRI AI

Introduction to AI
Silvana Amaral Kampel
Senior Researcher, INPE

AI for Forestry
Nipun Batra
Associate Professor, IIT Gandhinagar

AI for Buildings
Vaibhav Chugh
Lead - Technology and AI, Council on Energy, Environment and Water (CEEW)

Introduction to Climate Policy and Regulation
Benjamin Cook
Research Scientist, NASA Goddard Institute for Space Studies and Columbia University

Introduction to Climate Change
Jose Córdova-García
Associate Professor, ESPOL University

AI for Power & Energy Systems
Daniel Cusworth
Vice President, Carbon Mapper

AI for Carbon Accounting (Monitoring, Reporting, and Verification)
Virginia Dignum
Professor, Umeå University

Responsible & Ethical AI
Priya L. Donti
Assistant Professor, MIT | Co-founder and Chair, Climate Change AI

Tackling Climate Change with Machine Learning
Benjamin Kellenberger
Senior Scientist, EPFL

AI for Biodiversity & Ecosystems
Nagesh Kumar
Professor, Indian Institute of Science

AI for Water Resources and Hydrology
Binyu Lei
Assistant Professor, University of Birmingham

AI for Cities & Urban Planning
Dina Machuve
Co-Founder and CTO, DevData Analytics | Board Member, Data Science Africa

Shaping Your AI-for-Climate Project in Practice
Francisco Martin-Martinez
Senior Lecturer, King’s College London

AI for Accelerated Materials Science
Rendani Mbuvha
Associate Professor, University of the Witwatersrand | Co-founder, AfriClimate AI

AI for Weather
Nikola Milojevic-Dupont
Core Team, Climate Change AI

Sustainability Impacts of AI
Sonajharia Minz
Professor, Jawaharlal Nehru University

Combining AI and Indigenous Knowledge for Climate Action
Claire Monteleoni
Professor, University of Colorado Boulder | Senior Researcher, Inria | Co-founder, Climate Informatics

AI for Climate Science
Joyce Nakatumba-Nabende
Senior Lecturer, Makerere University | Board Member, Data Science Africa

AI for Public Health
Shruti Nath
Postdoctoral Research Associate, University of Oxford | Research Lead, AfriClimate AI

AI for Weather
Aliny Reis
Senior Remote Sensing Scientist, Corvian

AI for Agriculture and Food Security
Carlos Rodriguez-Pardo
Postdoctoral Researcher, Politecnico di Milano | Affiliated Researcher, CMCC Foundation

AI for Public Policy
David Rolnick
Associate Professor, McGill University and Mila - Quebec AI Institute | Co-founder and Chair, Climate Change AI

Tackling Climate Change with Machine Learning
Costa Samaras
Professor, Carnegie Mellon University

Introduction to AI Policy and Regulation
Mary Sanford
Research Fellow, University of Bath | Affiliated Researcher, CMCC Foundation

AI for Social Sciences
Maike Sonnewald
Assistant Professor, University of California Davis

AI for Oceans & Marine Systems
Maria João Sousa
Executive Director, Climate Change AI

Tackling Climate Change with Machine Learning
Mersedeh TariVerdi
Senior Data Scientist, The World Bank

AI for Risk Assessment, Disaster Management & Relief
Malte Toetzke
Postdoctoral Researcher, TU Munich | Senior Research Fellow, Max Planck Institute for Innovation and Competition

AI for Climate Finance
Cathy Wu
Associate Professor, MIT

AI for Transportation

Tutorial Creators

Tristan Ballard
Tristan Ballard
Zeus AI

AI for Climate Finance: Estimating Hurricane Wind Value-at-Risk from an AI Weather Ensemble
Eduardo Ulises Moya-Sanchez
Eduardo Ulises Moya-Sanchez
IIEG Jalisco

AI for Forestry
Jorge Montalvo Utkarsha Agwan Panos Moutis Enming Liang
Jorge Montalvo, Utkarsha Agwan, Panos Moutis, Enming Liang
City College of New York, City University of Hong Kong

AI for Optimal Power Flow
Jenny Hamer Rob Laber Tom Denton
Jenny Hamer, Rob Laber, Tom Denton
Google DeepMind, Google

Agile Modeling for Bioacoustic Monitoring
Hannah Kerner Caleb Robinson Isaac Corley Matthias Mohr Gedeon Muhaweyano Ivan Zvonkov Tristan Grupp Nathan Jacobs Akram Zaytar
Hannah Kerner, Caleb Robinson, Isaac Corley, Matthias Mohr, Gedeon Muhaweyano, Ivan Zvonkov, Tristan Grupp, Nathan Jacobs, Akram Zaytar
Arizona State University, Microsoft AI for Good Lab, Wherobots, Taylor Geospatial Engine, University of Maryland, World Resources Institute, Washington University in St. Louis

Agricultural Monitoring with Fields of The World (FTW)
Kingsley Nweye Allen Wu Hyun Park Yara Almilaify Zoltan Nagy Ava Mohammadi
Kingsley Nweye, Allen Wu, Hyun Park, Yara Almilaify, Zoltan Nagy, Ava Mohammadi
UT Austin, Eindhoven University of Technology

CityLearn: Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities
Douglas Mbura
Douglas Mbura
Lkotkote

Combining AI and Indigenous Knowledge for Climate Action
André Ferreira Isabelle Tingzon El Khalil Cherif
André Ferreira, Isabelle Tingzon, El Khalil Cherif
TransitionZero, RISE Research Institutes of Sweden, KTH Royal Institute of Technology, Institute For Systems and Robotics

Estimating Coal Power Plant Operation From Satellite Images with Computer Vision
Avinash Laddha
Avinash Laddha
data.org

Hands-On Outbreak Analytics: Dengue Forecasting with RAG and LLM Evaluation
Tarini Bhatnagar
Tarini Bhatnagar
NVIDIA

Introduction to AI - Predicting Tsunami Alerts from Earthquake Observations
Tedi Yankov
Tedi Yankov
University of Oxford

Monitoring Coastal Water Quality and Marine Ecosystem Change Using Satellite Remote Sensing and Machine Learning
Daniel Spokoyny Max Callaghan Tobias Schimanski
Daniel Spokoyny, Max Callaghan, Tobias Schimanski
Potsdam Institute for Climate Impact Research, MCC-Berlin, University of Zurich

NLP Models for Climate Policy Analysis
Josiah Kimani Oliver Angélil Chris Toumping Steffen Knoblauch
Josiah Kimani, Oliver Angélil, Chris Toumping, Steffen Knoblauch
AIMS South Africa, Ishango.ai, Heidelberg University

PiggyCast - Improving Weather Prediction Accuracy through a Stacking-Based Ensemble AI Approach
Kshitij Tayal Arvind Renganathan Siyan Liu Dan Lu Puja Das
Kshitij Tayal, Arvind Renganathan, Siyan Liu, Dan Lu, Puja Das
Oak Ridge National Labs, University of Minnesota, MIT

Planning for Floods & Droughts: Intro to AI-Driven Hydrological Modeling
Konstantin Klemmer Shafat Rahman Felix Wagner Florian Nachtigall Gabriela Yaulli Herrera
Konstantin Klemmer, Shafat Rahman, Felix Wagner, Florian Nachtigall, Gabriela Yaulli Herrera
TU Berlin, MCC Berlin, Cornell Tech

Predicting Mobility Demand from Urban Features
Mel Hanna Andrés Felipe Perez Murcia
Mel Hanna, Andrés Felipe Perez Murcia
Quanata, University of Manitoba

Reducing your Climate Impact when Training ML Models
Casper Fibaek Andreas Luyts Nirdesh Kumar Sharma
Casper Fibaek, Andreas Luyts, Nirdesh Kumar Sharma
ESA Φ-lab, EarthSense Labs

Sea Water Flood Risk Assessment in Egypt using Deep Learning, Sentinel-1 & 2, and Copernicus DEM
Clara Iglesias
Clara Iglesias
Climate Interactive

Simulating Climate Futures in En-ROADS
Jose González-Abad
Jose González-Abad
Instituto de Física de Cantabria

Statistical Downscaling of Climate Projections with Deep Learning

Schedule

Lectures will be held live at the times listed below, with recordings available shortly afterwards for those who are not able to attend live. Tutorials will be completed fully asynchronously. Participants do not need to attend all lectures – they are welcome to engage with specific lectures and tutorials based on their interest and availability. Note that the schedule is subject to change.

Week 1 - July 20-24, 2026

DateTime (UTC)LectureLecture Type
Mon, Jul 20 14:00-16:00 Tackling Climate Change with Machine Learning – Maria João Sousa, David Rolnick, Priya L. Donti Foundation
Tue, Jul 21 14:00–16:00 AI for Climate Science – Claire Monteleoni Sectoral
Wed, Jul 22 10:00-12:00 AI for Policy, Economics, and Social Sciences – Carlos Rodriguez-Pardo, Mary Sanford Sectoral
Thu, Jul 23 14:00-16:00 Introduction to AI – Tejumade Afonja Foundation
Fri, Jul 24 14:00-16:00 Introduction to Climate Change – Benjamin Cook Foundation
Fri, Jul 24 18:00-19:00 Introduction to AI Policy & Regulation - Costa Samaras Foundation

Week 2 - July 27-31, 2026

DateTime (UTC)LectureLecture Type
Mon, Jul 27 10:00–12:00 AI for Agriculture and Food Security - Aliny Reis Sectoral
Tue, Jul 28 10:00–12:00 AI for Buildings & Cities - Nipun Batra, Binyu Lei Sectoral
Wed, Jul 29 14:00–16:00 AI for Transportation - Cathy Wu Sectoral
Thu, Jul 30 10:00–12:00 AI for Water Resources and Hydrology - Nagesh Kumar Sectoral
Fri, Jul 31 10:00–12:00 AI for Weather - Rendani Mbuvha, Shruti Nath Sectoral

Week 3 - August 3-7, 2026

DateTime (UTC)LectureLecture Type
Mon, Aug 3 10:00–12:00 Responsible & Ethical AI - Virginia Dignum Foundation
Tue, Aug 4 10:00–12:00 AI for Forestry - Silvana Amaral Kampel Sectoral
Wed, Aug 5 10:00–11:00 Introduction to Climate Policy & Regulation - Vaibhav Chugh Foundation
Thu, Aug 6 18:00–20:00 AI for Oceans & Marine Systems - Maike Sonnewald Sectoral
Fri, Aug 7 14:00–16:00 Shaping Your AI-for-Climate Project in Practice - Dina Machuve Foundation

Week 4 - August 10-14, 2026

DateTime (UTC)LectureLecture Type
Mon, Aug 10 14:00–16:00 AI for Power & Energy Systems - Jose Córdova-García Sectoral
Tue, Aug 11 10:00–12:00 AI for Climate Finance - Malte Toetzke Sectoral
Wed, Aug 12 14:00–16:00 Sustainability Impacts of AI - Nikola Milojevic-Dupont Foundation
Thu, Aug 13 14:00–16:00 AI for Accelerated Materials Science - Francisco Martin-Martinez Sectoral
Fri, Aug 14 14:00–16:00 AI for Public Health - Joyce Nakatumba-Nabende Sectoral

Week 5 - August 17-21, 2026

DateTime (UTC)LectureLecture Type
Mon, Aug 17 14:00–16:00 AI for Biodiversity & Ecosystems - Benjamin Kellenberger Sectoral
Tue, Aug 18 14:00–16:00 AI for Risk Assessment, Disaster Management & Relief - Mersedeh TariVerdi Sectoral
Wed, Aug 19 14:00–16:00 Combining AI and Indigenous Knowledge for Climate Action - Sonajharia Minz Sectoral
Thu, Aug 20 18:00–20:00 AI for Carbon Accounting (MRV) - Daniel Cusworth Sectoral

Partners

Strategic & Content Partners

Network Partners

Organizers

On behalf of Climate Change AI:
Ana Maria Quintero Ossa (University College London)
Alamin Musa Magaga (Nest Africa AI Innovation Lab)
David Quispe (University of Toronto)
Amanda Murray (Duke University)
Olivia Zhang (University of Florida)
Oluwaferanmi Oladepo (Federal University of Technology, Akure)
Mary Salami (University of California, Santa Barbara)
Priya Donti (MIT)

Coordination Support: Ashley Eugley

Teaching Assistants: Akram Zaytar, Andrés Felipe Perez Murcia, Asbina Baral, Barbara Templ, Bernard Opoku, Chris Yeh, Christian Tran, El Khalil Cherif, Ellie Kim, Eloise Lopez, Gabriela Yaulli Herrera, Mohammed Bayero Yayandi, Nirdesh Sharma, Samuel Fernandes, Sara Badran, Sima Rahmani, Tarini Bhatnagar, Tariq Shahzad, Yazid Mikail, Ying-Jung Chen

Content Reviewers: Alamin Musa Magaga, Amanda Murray, Ana Maria Quintero Ossa, David Quispe, David Rolnick, Diyali Goswami, Ivan Poon, Jesse Dunietz, Konstantin Klemmer, Marcus Voss, Millie Chapman, Olivia Zhang, Panos Moutis, Peetak Mitra, Priya Donti, Puja Das, Sarah Skenazy, Tariq Shahzad, Veevee Cai, Weikang Qian

Interpreters: Kevin Mian, Angela Ortenzio, Anna Marchianò, Belkiss Adamo Issufo, Daniel Pedro Objane, Emanuele Dallasta, Enguene-Bikolo Nguini M-Noel Arlette, Fatinha Facinelli, Florencia Denise Ochoa , Gaia Frenquelli , Giorgia Giallombardo, Giulia Gavinelli, Héctor Rodríguez Rodríguez , Jiaxin Zheng, Kengne Defo Norine Andreas, Lea Filgueira López , Linda Curioni, Marcial González Ayela, Maribel Ercilla , Myriam Macrì, Naomi Gambo, Roberta Bosa , Sara Camellini, Sara Pasini, Ziemi Tchuidjang Yohanne Ashley

Frequently Asked Questions

Program Content and Focus

Q: What is meant by AI?
A: Broadly speaking, artificial intelligence (AI) encompasses any computer algorithm that makes predictions, recommendations, or decisions on the basis of a defined set of objectives. Many prominent examples of AI today are from a sub-area called machine learning (ML), which refers to techniques that infer patterns from examples (e.g., data). ML is used to describe a wide variety of techniques that range in their type and complexity, including, e.g., linear regression, decision trees, and deep neural networks; discriminative models vs. generative models; and task-specific models vs. general-purpose models.

Q: What types of AI will the CCAI Virtual Summer School focus on?
In the CCAI Virtual Summer School, our focus is on AI and ML algorithms broadly (as defined in the FAQ on “What is meant by AI?”) and their applications in climate change. The program is not focused on commercial AI assistants (e.g., ChatGPT, Claude, Gemini) or on agentic AI tools. Our aim is instead to give participants technical and socio-technical grounding in AI/ML methods and their climate-related applications.

Q: What areas of climate change will be covered?
A: Topics covered in the Summer School will span climate change mitigation (reducing or preventing greenhouse gas emissions), adaptation (building resilience and robustness to the effects of a changing climate), and climate science (understanding and predicting climate change).

Q: Are there prerequisites for the program? What prior knowledge is expected?
A: The program will begin with introductory lectures on AI and on climate change. The rest of the lectures will be presented to be accessible to individuals from a broad range of backgrounds, without any assumed prerequisites beyond the content of these introductory lectures. Hands-on coding tutorials will be presented in Python, and some prior familiarity with Python programming is assumed; self-study materials will be sent before the Summer School to help participants without prior background in Python programming acquire some introductory experience. Most tutorials assume only minimal prerequisite knowledge about AI, whereas some tutorials assume more substantial prior knowledge of AI. Prerequisites for individual tutorials will be flagged prior to the start of the Summer School.

Q: What sectoral modules will be available?
A: Currently, we anticipate providing sectoral modules on the following topics, though this is subject to change: AI for Accelerated Materials Science; AI for Agriculture and Food Security; AI for Biodiversity & Ecosystems; AI for Buildings & Cities; AI for Carbon Accounting (Monitoring, Reporting, and Verification); AI for Climate Finance; AI for Climate Science; AI for Forestry; AI for Oceans & Marine Systems; AI for Policy, Economics & Social Sciences; AI for Power & Energy Systems; AI for Public Health; AI for Risk Assessment, Disaster Management & Relief; AI for Transportation; AI for Water Resources and Hydrology; AI for Weather; Combining AI and Indigenous Knowledge for Climate Action.

Registration

Q: How can I register for the program?
A: You can register via a standard (paid) registration by filling out this form. After filling out the form, you will receive an email with payment instructions. After your payment is successfully processed, you will be onboarded to the CCAI Community Platform, which will be used for all program-related communications and to share program materials.

Q: What are the registration fees?
A: Registration fees are $30 (USD) for participants residing in high-income economies and $10 (USD) for participants residing outside high-income economies. We define “high-income economies” based on the World Bank country classification (see the header HIGH-INCOME ECONOMIES ($14,375 OR MORE) at this link).

Q: Are registration fees based on my country of residence or my country of origin?
A: Registration fees are based on your country of residence.

Q: Are fee waivers available (i.e., can I receive free registration)?
A: Yes, fee waivers are available, as we are eager to ensure that program fees are not a barrier to participation for any individual. You can request a fee waiver using this form. If you submit a fee waiver request, please do not separately complete the standard registration form.

Q: Are group registration options available?
A: Yes. Groups may pay in bulk for multiple registrations via this link. Please be sure to select the correct number of registrations and registration type based on the geography in which your participants are based. After submitting the group payment form, the payer will receive a bulk registration coupon code via email. Each program participant must also register separately using the standard registration form; when the participant receives their individualized payment link, they can use the bulk registration coupon code to bypass payment, after which they will be onboarded to the program.

Q: Does the CCAI Virtual Summer School accept paid registrations from all countries?
A: CCAI is unable to accept payments originating in the following countries and regions: Cuba, Iran, North Korea, the Crimea Region of Ukraine, the so-called Donetsk People’s Republic and Luhansk People’s Republic regions of Ukraine, Belarus, Russia, Syria, and Venezuela.

Individuals based in Belarus, Russia, Syria, and Venezuela who may otherwise be eligible to participate cannot submit paid registrations, and should instead apply for a fee waiver. See the FAQ on Eligibility for more details on who is eligible to participate.

Q: What is the deadline for registering?
A: Registration for the Virtual Summer School will remain open until the end of the program.

Logistics

Q: How do I access the program?
A: You will be prompted to join the Climate Change AI Community Platform and a platform called LearnWorlds upon completion of your registration. All information will be shared via these two platforms.

Q: At what times will lectures and tutorials be held?
A: Lectures will be held live according to the Schedule above, and will also be recorded. Coding tutorials are to be completed by participants in a self-paced manner (i.e., asynchronously) during the program.

Q: When will the schedule for the program be posted?
A: The schedule is now posted – see the Schedule section of the website.

Certificates and Time Commitment

Q: Will Climate Change AI provide certificates of participation for the program?
A: Yes. To get the certificate, participants must complete 6 foundation modules and at least 4 sectoral modules.

Q: Do I need to attend lectures live to obtain the certificate of participation?
A: No. Live lecture attendance is not required to receive a certificate. Participants will have access to recordings shortly after the live lectures.

Q: Until when can I complete certificate requirements?
A: Participants will have until September 20, 2026 to complete all certificate requirements, in order to receive a certificate of participation. Participants will also retain access to program content indefinitely after the program.

Q: What is the time commitment associated with the program?
A: Different participants can choose to engage with the program differently, which will affect the total time commitment. We anticipate that completing the requirements associated with the certificate of participation will take participants ~30 hours total (~6 hours/week) over the 5 weeks of the program (including lectures, tutorials, and asynchronous assignments). Alternatively, participants may choose to engage at a lower level of commitment if they so wish, i.e., engage with specific lectures and tutorials based on their interest and availability.

Eligibility

Q: Who is eligible to participate in the CCAI Virtual Summer School?
A: The CCAI Virtual Summer School is designed to bring together a global audience from across countries, sectors, and career stages. However, pursuant to United States federal laws and regulations, there are some restrictions on who may participate.

In accordance with United States export control laws and regulations, the following categories of persons and entities are not eligible to participate:

All participants must be at least 16 years of age. Participants under 18 (i.e., ages 16-17) must obtain consent from a parent or guardian to participate in the program.

Q: Are there any restrictions on participation based on my nationality or country of birth?
A: No. Eligibility restrictions are not based on nationality or country of birth. However, as described above, there are some restrictions based on an individual’s primary country of residence.

Q: What is the age requirement for the program?
A: All participants must be at least 16 years of age. Participants under 18 (i.e., ages 16-17) must obtain consent from a parent or guardian to participate in the program.

Other

Q: My question isn’t answered above. What should I do?
A: If you have a question not answered in the FAQs, please email summerschool+virtual@climatechange.ai. Due to the large number of inquiries, we may not be able to reply to all emails.