Climate Change AI Summer School 2022

The Climate Change AI summer school is designed to educate and prepare participants with a background in artificial intelligence and/or a background in a climate-change related field to tackle major climate problems using AI. The summer school aims to bring together a multidisciplinary group of participants and facilitate project-based team work to strengthen collaborations between different fields and foster networking in this space.

Dates and Application Information

About

The first part of the summer school will consist of a mix of lectures and hands-on tutorials organized into two tracks, one focused on AI fundamentals and one focused on climate change. In both tracks, the program will provide an overview of machine learning applications in a broad range of climate change-related areas. This includes covering foundational machine learning methods and state-of-the-art tools, while underlining their advantages and limitations, and describing how they can be used in practice to address the climate crisis. The second part of the summer school will consist of a collaborative project at the intersection of climate change and machine learning. Participants will work together in multidisciplinary groups under the guidance of a mentor to develop AI-based solutions for climate change problems.

Summer School Structure

The course is split into two parts. The first part of the course will consist of lectures and tutorials, and the second part of the course will consist of a group project.

The full course content and tutorial materials will be provided in English and the code exercises will use the Python programming language (for accepted participants who are not already familiar with Python, we will provide brief learning materials prior to the start of the course).

I. Lectures and Tutorials

The lectures and tutorials are organized into two tracks (AI-Focused and Climate-Focused, described below) designed to better provide an enriching and accessible learning experience to participants with different backgrounds.

AI-Focused Track
This track is designed for participants who have expertise in a climate change-related field but do not have a background in AI.

Content:

Learning outcomes: By the end of this track, students will be able to

Climate-Focused Track
This track is designed for students who have expertise in AI but may or may not have a background in a climate change-related field. Students applying for this track should have theoretical and practical experience in AI and be able to formulate AI approaches to new problems.

Content:

Learning outcomes: By the end of this track, students will be able to

II. Project

The project part of the course will consist of a group project at the intersection of climate change and machine learning. Participants from both tracks will collaborate in a multidisciplinary team and apply the skills they have learned throughout the summer school to develop a project focused on a specific use case. Each group will have the support of a mentor who will advise the team throughout the duration of the project, and help them identify next steps and “pathways to impact” for the work. Teams will have the opportunity to submit a proposal for their project to a future “Tackling Climate Change with Machine Learning” workshop at a premier machine learning conference.

Learning outcomes: By the end of the project, students will be able to:

Call for Participation

We welcome applications from students, researchers, engineers, and practitioners in the public and private sectors who are interested in using machine learning to address problems in climate change mitigation, adaptation, or climate science. The summer school is designed primarily for graduate students and professionals, but advanced undergraduate students are also welcome to apply. Participants should have taken at least one statistics course (e.g. equivalent to Introduction to Statistics), as well as a working knowledge of some programming language gained through formal or informal courses or projects. Applicants must be at least 18 years of age.

Applications are due by: Dec 17, 2021 23:59 AOE (Anywhere on Earth, UTC-12). To apply, please submit your application through this form: https://www.climatechange.ai/summer_school2022_application

The form is editable until the submission deadline. Please note that applications that are missing required information such as a CV at the application deadline will not be considered. If you’d like to prepare answers to the questions outside of this form, the questions are also available in a Google document here: https://www.climatechange.ai/summer_school2022_application_questions

The cohort will be composed of applicants from complementary areas of study/work, to be selected on the basis of their background and experience as well as their motivation for joining the summer school. Admission notifications will be sent out during the week of Feb 21, 2022.

The summer school is free to attend. Applicants who are accepted will be asked to confirm their attendance for the entire duration of the summer school. This course will be instructed by members of CCAI and world-renowned experts in ML and Climate Change. For further inquiries please contact summerschool@climatechange.ai

Organizers

On behalf of Climate Change AI,
Hari Prasanna Das (UC Berkeley)
Jeremy Irvin (Stanford)
Maria João Sousa (IST, ULisboa)
Olivia Mendivil Ramos (Climate Change AI)

Frequently Asked Questions

Q: Which areas are included for someone with an artificial intelligence background?
A: Areas include, but are not limited to, ML-relevant topics within:

Q: Which areas are included for someone with a climate change-relevant background?
A: Areas include, but are not limited to, climate-relevant topics within: