The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497
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Jul 1, 2021 
Claire Monteleoni, an associate professor at the University of Colorado Boulder, shares her inspiring journey from environmental activism to a leading role in climate informatics. The discussion covers innovative machine learning techniques for analyzing climate data, particularly unsupervised methods for downscaling. Claire also highlights the evolution of climate informatics conferences and their focus on collaboration. Additionally, she emphasizes the need for integrating social justice in climate action, advocating for resilience in vulnerable communities.
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From Activism to AI
- Claire Monteleoni's interest in environmentalism started in high school, organizing the first Environmental Awareness Day.
- Her interest in computer science developed later in college, leading her to study AI and machine learning.
Data Rich Environment
- Climate informatics offers a unique opportunity due to abundant publicly available data from sources like NASA and NOAA.
- This data richness, combined with climate scientists' computer skills, makes for efficient insights.
A Chance Meeting
- Claire Monteleoni's collaboration with climate scientist Gavin Schmidt began with a chance meeting where she repeatedly suggested machine learning applications.
- This led to their first collaboration using online learning to improve climate model predictions.