The Climate Change AI Summer School: 2023 Recap and 2024 Announcement
Find out more about CCAI’s flagship educational program and how you can learn to tackle climate change using machine learning.
At the end of August 2023, the Climate Change AI Summer School 2023 officially came to an end. The Summer School included both a selective in-person program held in Montreal and an open-to-all virtual program. The program provided resources and opportunities for participants with backgrounds in climate or machine learning related fields to tackle major problems at the intersection of climate and machine learning.
Over 10,000 registrants tuned in for the virtual program which taught participants about 11 different topics in the climate change and AI space. Through lectures and tutorials led by experts in these fields, participants gained insight and hands-on experience into the work being done in this domain. In-person participants worked together over the course of a week in Montreal to develop climate change solutions using AI and learn from top experts in the field.
In this blog post, we’ll share the details of the 2023 Summer School and how to participate in the 2024 Summer School.
Virtual Summer School 2023
The open-to-all virtual program spanned approximately 6 weeks and included lectures and tutorials on 11 different topic areas in the AI and climate change domain. Participants tuned in to the program from over 160 different countries. We are proud to serve a truly global audience. All of the lectures are publicly available on YouTube and have amassed nearly 60,000 views.
Experts from leading institutions like MIT, Columbia, Microsoft, Mila, Terra.do, ETH Zurich, and ESA Φ-Lab presented lectures and tutorials on AI for Transportation, AI for Climate Science, AI for Power Systems, AI for Agriculture, and more. One of the participants remarked that the depth and quality of these materials made the program “feel like a handbook” on applications of AI on climate. We are fortunate to have had such great instructors and tutorial developers contribute to the Summer School.
Along with the materials provided, the Summer School online community platform allowed for participants to interact with each other, asking questions and building relationships. Many participants were able to expand their network globally from the connections that they made during the program.
The 2024 Virtual Summer School will include additional materials and events. Participants will be challenged to think critically about some of the most pressing issues related to climate change and how machine learning can aid in finding solutions for these issues.
In-Person Summer School 2023
Interdisciplinary collaboration is key to developing solutions to climate problems using AI but it can often be challenging to work across disciplines effectively. Deploying those solutions into the real world and achieving measurable impact is even tougher. To help participants drive real change on climate change problems with AI, the CCAI In-Person Summer School focused on the key skills of collaboration, development, and deployment.
Participants had opportunities to collaborate virtually prior to the start of the Summer School, but the real work began in Montreal. In only a week, each team was able to develop, at a minimum, a proof of concept of their idea. The participants also attended learning exercises from experts in academia and industry related to communicating impact effectively, pitching, responsible AI, and evaluating a project.
“The Summer School was a turning point in my career. The experience, the inspiration, the opportunity provided and knowledge I acquired are indelible and eternal. It brought experts across-the-board globally in one place for idea generation and fertilization. I made new friends, networks, established collaboration and increased my social capital via the incredible people I met in the summer school.” - In-Person Summer School Participant
The teams worked on incredibly interesting projects and quickly put into practice the concepts that they learned throughout the week. On the final day of the Summer School, they put their ideas to the test by pitching their projects in front of a panel of experts.
The projects focused on topics like predicting flooding in coastal areas, optimizing Cassava yield in Nigeria, and forecasting land degradation in Colombia. Many of the teams are continuing to work together on their projects, with some earning acceptance of their papers and proposals at venues like the Tackling Climate Change with Machine Learning workshop at the Conference on Neural Information Systems Processing (NeurIPS). We can’t wait to see how these projects continue to progress!
We’d like to thank CIFAR, DENVR Dataworks, Mila, Volkswagen Group of America, and Worldsphere.ai for their support of the CCAI Summer School. Without them, the program would not be possible.
We are immensely grateful for all of those who participated in and contributed to this program. We are encouraged by the support that the Summer School received and will continue to build on this success in the future to further our goal of democratizing education in the climate change and AI space.
Below you will find more information about some of the 2023 in-person Summer School projects, mentors, and participants as well as how to participate in the 2024 Summer School.
Summer School 2024
We are excited to host the 3rd edition of the CCAI Summer School in 2024. As in 2023, the 2024 Summer School will include both an open-to-all virtual program and a selective in-person program. The virtual program will feature another stellar lineup of instructors and will cover additional topics that weren’t included in the 2023 program. There will also be many opportunities to collaborate with other participants and interact with instructors and tutorial developers.
The in-person program will bring a new cohort together to develop solutions to climate change-related issues using machine learning. If you want to enhance your ability to work in interdisciplinary teams at the intersection of climate change and AI, receive training from experts in this space, and develop global connections, apply here.
For more information about the 2024 Summer School and to register for the 2024 Virtual Summer School click here.
Teams and Team Members
Forecasting Land Degradation in Colombia
Wenxin Yang
PhD Student in Geography at Arizona State University
Wenxin is interested in the intersection of spatial data science and conservation. Her research centers on the effectiveness of protected areas in terms of monitoring, reporting, and predicting future trends, and informing conservation planning decision-making with spatial data science. Climate change has been impacting and will continue to impact protected areas efficacy whereas its trend and mechanism remains understudied and poorly integrated into conservation planning. Wenxin is interested in leveraging ML algorithms to promote forward and future looking conservation planning by developing novel datasets and models to start filling the research gap.
Tabea Stoeckel
Sustainability and Climate Change Consultant at PwC
Tabea has a background in environmental policy and how to use emerging technologies to fight climate change. After Tabea studied international affairs in Switzerland where she grew up, she worked for the Swiss government for 2 years in San Francisco on innovation policy, where she first worked on AI by organising a workshop around ethical AI. She then pursued a master’s in Environmental Technology at Imperial College London and started working for PwC’s Sustainability and Innovation team in London, where she co-authored several reports around e.g. the sustainable use of emerging technologies, i.e. blockchain, and how to direct funding to more impactful climate technology solutions.
Precipitation Intelligence
Matthias Bittner
PhD Candidate at the Christian Doppler Laboratory for Embedded Machine Learning at TU Wien
Curiosity and empathy have always been the driving forces in Matthias’s life.
During the journey from a student to a fully educated electrical engineer and ML scientist, his curiosity was strongly influenced by feeling the need to have a positive impact.
Right now, he is a PhD candidate at the Christian Doppler Laboratory for Embedded Machine Learning at TU Wien. There, he focuses on developing resource-efficient (low energy, latency, storage) machine learning time series applications, such as, forecasting grid loads, or precipitation downscaling of global climate models.
Sanaa Hobeichi
Postdoc at University of New South Wales (UNSW) Sydney
Sanaa works on applying Machine Learning to advance Climate Change research. Her current work is focused on developing Machine Learning methods for downscaling climate data and improving predictions of climate extremes. She is interested in explainable and physics-informed machine learning.
Sanaa obtained her PhD in Climate Science from UNSW, and she holds a MSc in Environmental Remote Sensing and a BSc in Computer Science and Applied Mathematics.
Ozioko Remigius Ikechukwu
Junior Lecturer at the University of Nigeria
Ozioko Remigius is from Nsukka in Enugu State, South East Nigeria. Nsukka is a beautiful soft-green rolling hilly City with very clement weather all through the year. It harbors the University of Nigeria Nsukka where he works as a Junior Lecturer. As a PhD student, his teaching and research revolves around Climate change governance and policy, food systems, community sustainability and engagement-nexus. Currently, he integrates AI and ML in developing solutions to the challenges of climate change to food production in Nigeria. His research team has deployed AI in the management, production and marketing of treasured vegetables in Nigeria.
Flooding Assessment for Coastal Environments (FACE)
Debasish Mishra
PhD Student in Biological and Agricultural Engineering at Texas A&M University
Debasish’s research focuses on studying the impact of extreme weather events on the soil-plant-atmosphere system using global satellite data. He aims to develop a data fusion model to predict flash floods and droughts, while also investigating the underlying scaling behaviors and controlling factors of these hydroclimatic extremes.
Tarini Bhatnagar
Solutions Architect at NVIDIA
A Geophysicist turned data professional; Tarini is a Solutions Architect at NVIDIA working on the frontiers of Artificial Intelligence. She leads technical customer engagements in Western Canada helping them adopt NVIDIA technology. She comes from a background in Earth and Environmental Science with extensive research experience and wanted to leverage my knowledge of both fields. Earlier such opportunities were limited, however with increasing relevance of Climate Change, new avenues have opened enabling her to contribute to this field. With so much Earth Observation data at hand, accelerated computing will help to achieve breakthroughs and I want to be a part of that effort.
Yusuf Ogunfolaji
MSc Student at the University of Ljubljana
Yusuf is from Nigeria and a second-year MSc student at the University of Ljubljana, Slovenia, studying Environmental Engineering.
During the first year of his double master’s degree at the University of Calabria in Italy, he assessed the impact of climate change on reservoir functionality using the HBV model to simulate the catchment runoff.
This experience triggered his interest in addressing this climate change impact using AI/ML as an alternative to the hydrological model.
Cassava Yield Optimization in Nigeria
Oluwaferanmi Oladepo
Undergraduate Student at the Federal University of Technology, Akure
Oladepo Oluwaferanmi is a Nigerian undergraduate at the Federal University of Technology, Akure, and a freelance software developer. As a member of the Oyo State Youth Parliament, he represents local youth, focusing on sustainable development and climate advocacy. This role exposed him to rural communities’ climate challenges and led him to discover AI’s potential in addressing them. He’s since become passionate about AI for climate solutions and has begun learning and applying AI techniques in this context, aiming to make a meaningful impact.
Gisa Murera
Senior Data Scientist at the National Bank of Rwanda, Kigali City
Gisa is a senior data scientist at the National Bank of Rwanda, Kigali City in the heart of Africa. He is passionate about harnessing AI and remote sensing for agricultural sustainability. Hailing from Rwanda, He’s been involved in data-driven research for years. His journey into climate and AI work began with a desire to address the challenges facing the agriculture sector and dynamic food prices. Currently, his focus is on leveraging the power of satellite imagery and seasonal agriculture surveys to develop optimal predictive machine-learning models for crop yield estimation and prediction. I am dedicated to using cutting-edge technology to drive sustainable agricultural practices in Rwanda and beyond.
Yazid S. Mikail
Data Scientist, Policy, Climate Change, and SDGs Advocate
Yazid has a professional certification in; Environmental Education and Community Engagement from Cornell University, Project Management Professional from the University of Virginia Darden Business School, and Leadership training from Harvard University. Yazid is from a semi-urban community named Dogarawa in Kaduna State Nigeria. Yazid had firsthand experience of the impact of climate change at 6 when his family’s house and farm were flooded like many other people in his community. Excited about the potential of technology in solving such problems inspired Yazid to link his firsthand experience to his book knowledge of data science, and climate change activism to explore the field of AI and ML for climate change.
WisePower Living
Donna Vakalis
Postdoc at Mila - Quebec Artificial Intelligence Institute
Donna worked in the fields of architecture and engineering before joining Mila, where she is currently a postdoc co-supervised by Dr. Yoshua Bengio and Dr. David Rolnick. Her research merges artificial intelligence with architectural engineering practices, toward mitigating emissions from new and existing buildings. She is also a former two-time Olympian and now the Scientific Lead for Racing to Zero, a non-profit that assists sport organizations to understand and reduce their negative environmental impact.
Elena Fillola Mayoral
PhD Student at the University of Bristol
Elena is a PhD student at the University of Bristol, working on using ML to accelerate greenhouse gas emissions monitoring. Her current research revolves around emulating atmospheric dispersion models, enabling more efficient simulations of the movement of gases like methane in the atmosphere. Elena enjoys working in interdisciplinary teams, using her background in Machine Learning to solve problems across a wide range of areas. In particular, she’s interested in Earth Sciences and in projects that have real-world impact.
Alina Klerings
PhD Candidate at University of Mannheim
Alina is from Germany and holds a Master’s degree in Data Science. Her growing concern with climate change got her interested in the intersection of machine learning and climate related applications. Over the past years she was involved in various forecasting projects in the energy domain, including grid load and net loss prediction as well energy disaggregation. She has industry experience through her work at a transmission system operator and this fall she started her PhD in AI Systems.
High Impact Climate and Weather Events Detection
Arbel Yaniv
Electrical engineering Ph.D. candidate at Tel Aviv University, Israel
Equipped with extensive background in algorithms and machine learning implementations & research in the energy field including power flow, distribution systems optimal operation, photovoltaic systems, optimal storage control and non-intrusive load monitoring (NILM).
Constanza Molina
PhD Student at the University of Munster
Constanza is from Chile and completed her master’s degree at the Pontifical University of Chile, where she worked with physics-informed neural networks to model global surface dust deposition during the Holocene and Last Glacial Maximum periods. Currently, she is pursuing her PhD at the University of Munster in Germany, and is part of the WilDrone project. This program is dedicated to wildlife conservation through the utilization of autonomous drone technology and AI for monitoring wildlife populations, tracking their movements, and managing human-wildlife conflicts.
Rosa Pietroiusti
PhD candidate at the Vrije Universiteit Brussel, Belgium and the University of Warwick, UK.
Rosa is investigating how climate science research is being used as evidence in child and youth-led litigation around the world. She is focusing on fields including impact attribution, extreme event attribution and intergenerational analysis of global climate and impact models.
Benedetta Mussati
Student in the AIMS DPhil program at the University of Oxford
Benedetta is an Italian student in the AIMS DPhil program at the University of Oxford, researching continual meta-learning algorithms.
She sees machine learning as a very helpful tool for climatologists to get a richer and clearer understanding of the data available and make more informed decisions. ### Vegetation Forecasting for Efficient Carbon Markets
Dr. Sylvia Smullin
Head of R&D at VEIR
Dr. Sylvia Smullin is an experimental physicist with more than a decade of experience at the early stage of research and development. Motivated by the magnitude and urgency of the challenge, Dr. Smullin chose a career in industry so as to work on impactful climate solutions. Currently the Head of R&D at VEIR (veir.com), she has worked at both bigger companies (PARC, Google X) and at startups (Makani Power, Form Energy). Collaborations at Alphabet sparked her curiosity about and appreciation for AI tools. Across technologies, Dr. Smullin brings a passion for collaboration, data analysis, and impact-focused work.
Dr. Mike Smith
Director of AI at Aspia Space
Dr. Mike Smith is originally from Hatfield – a small town on the outskirts of London – and has a background in astronomy and physics, with his masters concentrating on using deep learning to diagnose heart disease and his PhD concentrating on applying deep generative models to extract information from crazily huge (petabyte-scale!) astronomical survey data. Now he’s working as the Director of AI at a geospatial start-up that spun out of his PhD research. At Aspia Space they use deep learning to extract useful information from remote sensing imagery (like foundational modelling for drought and flood prediction, crop identification, and cloud cover removal from satellite imagery).
Ricardo Barros Lourenço
PhD Student at McMaster University
Ricardo is a Brazilian Geoscientist pursuing his PhD at McMaster University in Canada, where he works as a Research Assistant in Geography. His academic journey has been a rich tapestry—starting in geology, moving through a master’s in data science, and advancing into computational science for remote sensing. He also lived in Chicago for almost five years, deeply involved in research using generative models for spatial tasks. Currently, his passion lies in crafting explainable models for vegetation dynamics under various climate change scenarios. Methodologically, his work leans on time series analysis, ensemble learning, and neural networks to make spatial computing more understandable and impactful.
Sustainability Report Chatbot (Susi)
David Denny
Co-Founder and Partner at Carpe Diem Developers
David has worked in the solar industry for the past twelve years. He has founded and led multiple companies that collectively have installed several thousands solar panel systems. He is committed to powering the renewable revolution, and sees AI as a tool to lower the cost and increase the pace of our clean energy transition.
Flora Haberkorn
Data Scientist at the Federal Reserve Board
Flora Haberkorn is currently working as a Data Scientist for the Federal Reserve Board (FRB) in the division of International Finance. She specializes in big data engineering and AI/ML techniques. She typically works with LLMs across a variety of domains such as climate, trade, and macroeconomics. Her research interests focus on the application of artificial intelligence for economic policy and research projects. She became a Data Scientist after pursuing the use of advanced analytics to enhance ongoing news and social media analysis as a Research Assistant at the FRB.
Sara Badran
MENA Regional Coordinator at Thought For Food
Sara Badran, holding a Bachelor’s degree in Food Science and Management and a Master’s in Environmental Sciences, is dedicated to leveraging her expertise to drive innovation and foster sustainability, particularly in addressing climate change. Serving as the MENA Regional Coordinator at Thought For Food, Sara passionately supports innovative and entrepreneurial endeavors. Her experience includes diverse projects in waste reduction, and food supply chain optimization through participation in accelerators. Sara’s research extends to applying machine learning models in her thesis, focusing on the circular economy’s potential in waste management and reuse, showcasing her commitment to sustainable solutions.
Mentors
Sharon Xu
Senior Data Scientist at Indigo Ag
Sharon is a senior data scientist at Indigo Ag, where she is designing deep learning models to detect regenerative practices and inform carbon credit generation. In previous roles, she has led spatial modeling R&D for field experimentation, designed and productionized scalable ML models at Tripadvisor, proposed novel methods to model human dynamics at MIT, and prototyped graph CNNs to identify security incidents at Element AI.
Arthur Ouaknine
Postdoctoral Researcher Fellow at McGill University and Mila - Quebec Artificial Intelligence Institute
After completing his PhD in the automotive industry in collaboration between Institut Polytechnique de Paris and valeo.ai, he wanted to use artificial intelligence to better understand the biodiversity and climate crises. His projects are now focused on computer vision and deep learning applied to forest monitoring. He also a core team member of Climate Change AI leading the webinar team.
Millie Chapman
Postdoc Fellow at the National Center for Ecological Analysis and Synthesis
Millie recently completed her PhD at University of California Berkeley in Environmental Science, Policy, and Management and is currently Postdoc Fellow at the National Center for Ecological Analysis and Synthesis. Her research is at the intersection of decision theory, ecology, and data justice, asking how we can leverage AI to devise more effective and equitable strategies to meet global biodiversity targets under uncertainty.
PhD student at the California Institute of Technology
Christopher Yeh is a 4th-year PhD student in the Computing and Mathematical Sciences (CMS) program at the California Institute of Technology (Caltech). Advised by Professors Yisong Yue and Adam Wierman, Chris’s research combines theoretical tools from uncertainty quantification and online decision-making with applications in sustainable energy systems. He is a Caltech Resnick Sustainability Institute Scholar and is funded by a Caltech Amazon AWS AI for Science Fellowship. He received B.S. and M.S. degrees in computer science from Stanford University where he worked with Professor Stefano Ermon, and he also spent a year in Beijing as a Schwarzman Scholar at Tsinghua University.