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 Announcements

Schedule

August 15th

Time (UTC) Time (Local) Event
Welcome and Opening Remarks
Tackling Climate Change with Machine Learning
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Lynn Kaack, Priya Donti, David Rolnick
  • TAs: Maria João Sousa and Hari Prasanna Das
Parallel Sessions
Details: (click to expand)
  • Introduction to Machine Learning (AI-Focused Track)
    • Location: Zoom Room 1
    • Speaker: Zico Kolter
    • TA: Jeremy Irvin
  • Introduction to Climate Change (Climate-Focused Track)
    • Location: Zoom Room 2
    • Speakers: Sonia I. Seneviratne, Robbert Biesbroek, Felix Creutzig
    • TA: Kelly Kochanski, Nikola Milojevic-Dupont
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Jeremy Irvin and Daniel Spokoyny

August 16th

Time (UTC) Time (Local) Event
Check-in
Measuring Progress under the Paris Agreement
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Evan Sherwin
  • TAs: Kureha Yamaguchi
Tutorial - NLP in research synthesis in the field of climate change
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Max Callaghan
  • TAs: Kureha Yamaguchi
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Hari Prasanna Das

August 17th

Time (UTC) Time (Local) Event
Check-in
Climate Science
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Kasia Tokarska
  • TAs: Ankur Mahesh
Tutorial on Forecasting El Niño/ Southern Oscillation
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Ankur Mahesh
  • TAs: Kelly Kochanski
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Ankur Mahesh

August 18th

Time (UTC) Time (Local) Event
Check-in
Buildings
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Marcus Voss, Javier Arroyo, David Blum, Kyle Benne and Iago Cupeiro
  • TAs: Hari Prasanna Das
Tutorial on Building Load Forecasting with Machine Learning
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Marcus Voss
  • TAs: Hari Prasanna Das
Tutorial on Building Control with RL using BOPTEST
Details: (click to expand)
  • Location: Zoom Room 2
  • Speakers: Javier Arroyo, David Blum, Kyle Benne and Iago Cupeiro
  • TAs: Hari Prasanna Das
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Marcus Voss
Social Hour
Details: (click to expand)
  • Location: Zoom Room 2
  • Social Hour for Cohort and Mentors, led by Mark Roth. Here you will have the chance to virtually meet your classmates and your project mentors.

August 19th

Time (UTC) Time (Local) Event
Check-in
Forestry and other land use
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: David Dao
  • TAs: Maria João Sousa and Jeremy Irvin
Agriculture
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Catherine Nakalembe
  • TAs: Maria João Sousa and Jeremy Irvin
Tutorial on Land Use and Land Cover (LULC) classification using Pytorch
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Isabelle Tingzon
  • TAs: Maria João Sousa and Jeremy Irvin
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Maria João Sousa and Jeremy Irvin

August 22nd

Time (UTC) Time (Local) Event
Check-in
Impact Assessment of Machine Learning
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Lynn Kaack, George Kamiya, Alexandra Sasha Luccioni
  • TAs: Jeremy Irvin
Team Project Working Time
Details: (click to expand) Location: TBD with your team
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Jeremy Irvin, Hari Prasanna Das and Daniel Spokoyny

August 23rd

Time (UTC) Time (Local) Event
Check-in
Power Systems
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Nsutezo Simone Fobi and Priya Donti
  • TAs: Hari Prasanna Das
Team Project Working Time
Details: (click to expand) Location: TBD with your team
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Hari Prasanna Das

August 24th

Time (UTC) Time (Local) Event
Check-in
Transportation
Details: (click to expand)
  • Location: Zoom Room 1
  • Speakers: Konstantin Klemmer and Nikola Milojevic-Dupont
  • TAs: Kureha Yamaguchi
Team Project Working Time
Details: (click to expand) Location: TBD with your team
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Daniel Spokony

August 25th

Time (UTC) Time (Local) Event
Check-in
Panel Discussion on Deploying Machine Learning for Climate Action
Details: (click to expand)
  • Location: Zoom Room 1
  • Panelists: Rachel Engstrand (Pachama), Campbell Watson (IBM Research) and Liuca Yonaha (Política por Inteiro)
  • TAs: Maria João Sousa and Hari Prasanna Das
Team Project Working Time
Details: (click to expand) Location: TBD with your team
Office Hours
Details: (click to expand)
  • Location: Zoom Room 2
  • TAs: Maria João Sousa

August 26th

Time (UTC) Time (Local) Event
Check-in
Presentation I
Details: (click to expand)
  • Location: Zoom Room 1
  • Every Team will present in 4 mins their proposal to the cohort, TAs and some members of the advisory board of Climate Change AI.
  • TAs: Ankur Mahesh, Jeremy Irvin
Presentation II
Details: (click to expand)
  • Location: Zoom Room 1
  • Every Team will present in 4 mins their proposal to the cohort, TAs and some members of the advisory board of Climate Change AI.
  • TAs: Ankur Mahesh, Jeremy Irvin
Farewell and Social Hour for the Cohort
Details: (click to expand) Location: Zoom Room 2

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.

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

Note: The deadline to apply for this summer school was Dec 17, 2021, and is already over. We have finalized our cohort and are no longer accepting applications. We look forward to your application for the next iteration of this summer school.

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: