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Endless Summer School: Sustainable AI
May 3 @ 10:00 am - 12:00 pm
Vector Researchers will present the latest advances for more efficient models that can reduce training time, compute resources, and data requirements. Tegan Maharaj will be presenting a broad overview of the ways AI can help address the climate crisis. Meredith Franklin will talk about her work applying statistical methods to climate data, Jesse Hoey about the general limitations of statistical machine learning and his work on the social-psychological factors of climate change, and James Lucas about his work optimizing AI models.
Talks, Moderated by Deval Pandya
AI to address environmental challenges: clean air and climate change
by Meredith Franklin, Associate Professor, Department of Statistical Sciences & School of the Environment, University of Toronto
Satellite imagery and global circulation models (GCM) have vastly improved our understanding of earth processes. Both provide sources of data that are invaluable for assessing environmental factors such as air quality and climate change, but at significant monetary and computational cost. We illustrate the use of deep learning approaches to enhance satellite and GCM data, with particular focus on generating high-resolution products that can be used in downstream applications such as environmental sustainability and health effects assessments.
Socially Sustainable Machine Learning with Application to Climate Change
by Jesse Hoey, Vector Institute Faculty Affiliate, Professor, Department of Computer Science, University of Waterloo
A major challenge for machine learning and artificial intelligence is dealing with surprising and unpredictable events. Climate change will produce such surprises as tipping points are reached and novel situations arise. In this talk, Jesse Hoey explores efforts to use computational models of emotion to build adaptable machine learning for climate change mitigation strategies focused on human behavior. An important element of these systems is explainability, including narratives generated around issues of fairness and diversity.
Tackling Climate Change with Machine Learning: An Overview by Tegan Maharaj, Assistant Professor, Affiliate Member of Schwartz Reisman Institute for Technology and Society, Affiliate Member of Vector Institute
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Machine learning is not a silver bullet in addressing the climate crisis, but ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change, and in this talk I present an overview of our review paper detailing many ways ML researchers can take action and apply their skills, as well as important considerations to keep in mind in doing so.
Looking towards sustainability when training deep learning models
by James Lucas, Research Scientist, NVIDIA Toronto AI Lab
The recent success of deep learning is coupled with an explosion in the size of neural networks and the size of the data sets that they are trained on. As a direct consequence, we require more computational resources to train and deploy our models and are obligated to carefully consider the most sustainable methods to achieve this. In this talk, I will present an overview of optimization techniques to train large models more quickly and methods to use data more efficiently during training.
This event is open to Vector Sponsors, Vector Researchers, and invited health partners only. Any registration that is found not to be a Vector Sponsor, Vector Researcher or invited health partner will be asked to provide verification and, if unable to do so, will not be able to attend the event. Please contact email@example.com with any questions.
Dr. Jesse Hoey is a professor in the David R. Cheriton School of Computer Science at the University of Waterloo, where he leads the Computational Health Informatics Laboratory (CHIL). He is a Faculty Affiliate at the Vector Institute, and an affiliate scientist at KITE/TRI, both in Toronto. Dr. Hoey holds a Ph.D degree (2004) in computer science from the University of British Columbia. He has published over one hundred peer reviewed scientific papers. His primary research interest is to understand the nature of human emotional intelligence by attempting to build computational models of some of its core functions, and to apply them in domains with social and economic impact. He is an associate editor for the IEEE Transactions on Affective Computing.
Tegan Maharaj is an Assistant Professor in the Faculty of Information at the University of Toronto, an affiliate of Vector and of the Schwartz-Reisman Institute. She is also a managing editor at the Journal of Machine Learning Research (JMLR), the top scholarly journal in machine learning, and co-founding member of Climate Change AI (CCAI), an organization which catalyzes impactful work applying machine learning to problems of climate change. During PhD at Mila and Polytechnique Montreal, she was an NSERC and IVADO awarded scholar with Chris Pal. Tegan is interested in studying “what goes into” AI systems – not only data, but the broader learning environment including task design and specification, loss function, and regularization; and the broader societal context of deployment including privacy considerations, trends and incentives, norms, and human biases. She is concerned and passionate about AI ethics, safety, and the application of ML to environmental management, health, and social welfare.
James Lucas is a Research Scientist at NVIDIA within the Toronto AI lab. He is also a PhD candidate at the University of Toronto, where he is supervised by Richard Zemel and Roger Grosse. James has a broad set of research interests within deep learning and ML more broadly. His research has focused on understanding and improving the optimization of deep neural networks. In particular, he has worked towards understanding the loss landscape geometry of these models and how we may leverage their properties in practice. He is also excited by deep generative models for 3D geometry and improving the data-efficiency of deep learning systems.
Deval Pandya is Director of AI Engineering at Vector Institute and Expert member of the task force in digitalization of energy systems at UN economic commission of Europe (UNECE). He is passionate about building Responsible Artificial Intelligence and Machine learning systems. He and his team are focused on building tools and implementations that lower the barrier to adoption of breakthrough AI research at Vector. Prior to joining Vector, Deval was leading the Data Science team at Shell focusing on application in New Energies and Asset management. During his career, he has led development of scalable machine learning applications in the domains of nature-based solutions, predictive maintenance, e-mobility, microgrid optimizations and hydrogen value chain. He mentors and advises various startups at the intersection of AI and Climate change and is also a mentor at Creative Destruction Labs. Deval always makes time to cook and travel