David Rolnick

David Rolnick is an Assistant Professor and Canada CIFAR AI Chair at McGill University's School of Computer Science and Mila – Quebec AI Institute. His research focuses on applying machine learning to address climate change challenges, including biodiversity monitoring, land use classification, climate modeling, and materials discovery. He co-founded Climate Change AI and leads initiatives like the NSF-NSERC Global Center on AI and Biodiversity Change (ABC). Rolnick's work emphasizes developing transfer learning and meta-learning techniques to handle sparse data in climate-related applications, such as predicting energy demand and simulating cloud formations. He has been recognized with the Sloan Research Fellowship and MIT Technology Review's '35 Innovators Under 35' distinction.

Education: Postdoctoral research at the University of Pennsylvania (NSF Mathematical Sciences Postdoctoral Fellow), PhD from MIT (NSF Graduate Research Fellow).

Research interests span AI applications in climate science, including energy systems optimization, satellite imagery analysis for environmental monitoring, and causal representation learning in climate data. His lab explores ethical AI deployment, particularly mitigating negative applications like oil exploration while addressing energy consumption concerns in ML.

Key projects include ClimART (a climate radiative transfer benchmark), OpenForest (a forest monitoring data catalog), and collaborations on fusion energy modeling. Awards include the AI2050 Fellowship for transformative research impact.

• Awards: Sloan Research Fellow, AI2050 Fellow, MIT TR35
• Grants: NSF-NSERC Global Center, Climate Change AI initiatives
• Labs/Teams: McGill Climate AI Group, Mila collaborations