Recorded 04 February 2026. Maria Molina of the University of Maryland presents "Learning Without Labels: New Insights into Climate and Extremes" at IPAM's Mathematics and Machine Learning for Earth System Simulation Workshop. Abstract: Climate variability and weather extremes pose profound challenges for prediction, preparedness, and resilience. Traditional approaches often rely on predefined indices or supervised learning methods, which can overlook unexpected patterns or reinforce biases inherent in labeled datasets. This talk explores how unsupervised learning techniques can uncover hidden patterns in high-dimensional climate data. I will highlight recent innovations that adapt established methods to reveal properties not captured by conventional architectures, offering new perspectives on modes of variability and extreme events. For instance, a knowledge-guided autoencoder can disentangle distinct Pacific climate modes with differing spectral signatures, while a custom hyperparameter search can optimize self-organizing maps to produce smooth, interpretable pathways among weather regimes. Together, these advances help uncover processes and mechanisms that may underlie established climate and weather phenomena. Ultimately, unsupervised learning provides a powerful lens for scientific discovery, with implications for understanding, prediction, and decision-making in a changing climate. Learn more online at: https://www.ipam.ucla.edu/programs/workshops/mathematics-and-machine-learning-for-earth-system-simulation/?tab=overview