David Rolnick

David Rolnick is an Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at Mila – Quebec AI Institute. His research focuses on Machine Learning, Deep Learning Theory, and Climate Change, with an emphasis on innovations driven by problems in climate change, including biodiversity monitoring, land use classification, climate model emulation, and materials discovery. He also serves as Co-founder and Chair of Climate Change AI, Scientific Co-director of Sustainability in the Digital Age, and co-lead of the NSF-NSERC Global Center on AI and Biodiversity Change (ABC).

David Rolnick is a researcher at McGill University and the Mila Quebec AI Institute, focusing on Machine Learning, Climate Change, Biodiversity, and Deep Learning Theory. His work explores the application of machine learning to address global challenges such as climate change and biodiversity loss. He investigates deep learning theory, including optimization, continual learning, and network complexity. His research also includes methods for improving the robustness and efficiency of deep learning models, as well as their use in remote sensing and environmental analysis. Rolnick's publications highlight the potential of AI to contribute to climate modeling, biodiversity monitoring, and sustainable development.