Climate Change AI Summer School 2022

About Schedule Organizers FAQ

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

Welcome Packet including Onboarding Information, and Logistics have been sent, as of Aug 9th, 00:00 UTC.
Date: Aug 15-26, 2022 (Weekdays only)
Location: Virtual
Contact: summerschool@climatechange.ai

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: João Dinis (EMAC), 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:

Introductory lectures on AI and its applications to climate-relevant topics
Hands-on tutorials including code-based exercises covering the application of machine learning methods to problems relevant to climate change using modern tools

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

Demonstrate an understanding of machine learning foundations
Apply machine learning techniques to climate change problems

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:

Introductory lectures on climate change mitigation, adaptation, and climate science, and relevant AI applications in this space
Hands-on tutorials with practical examples covering various opportunities and challenges to address climate change with AI tools

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

Demonstrate an understanding of climate change mitigation, adaptation, and climate science problems
Use basic and advanced machine learning techniques to solve climate change problems

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:

Formulate a machine learning approach to tackle a climate change-related problem
Use machine learning to develop a potential solution to the problem
Engage, discuss, and collaborate among teammates with complementary expertise

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,
Ankur Mahesh (UC Berkeley)
Daniel Spokoyny (CMU)
Hari Prasanna Das (UC Berkeley)
Jeremy Irvin (Stanford)
Kelly Kochanski (Opendoor Labs)
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:

Active learning
Causal and Bayesian methods
Classification, regression, and supervised learning
Computer vision and remote sensing
Data mining
Generative modeling
Hybrid physical models
Meta- and transfer learning
Natural language processing
Recommender systems
Reinforcement learning and control
Time-series analysis
Uncertainty quantification and robustness
Unsupervised and semi-supervised learning

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:

Agriculture and food 
Behavioural and social science
Buildings
Carbon capture and sequestration
Cities and urban planning
Climate finance and economics
Climate justice
Climate science and climate modeling
Disaster management and relief
Earth observations and monitoring
Earth science
Ecosystems and biodiversity
Extreme weather
Forestry and other land use
Health
Heavy industry and manufacturing
Local and indigenous knowledge systems
Materials science and discovery
Oceans and marine systems
Power and energy systems
Public policy
Societal adaptation and resilience
Supply chains
Transportation