Dr. Elizabeth A. Barnes, Boston University
YouTube Stream: https://www.youtube.com/watch?v=5X98rg8zaI8
Join group to receive calendar invite: https://groups.google.com/a/modelingtalks.org/g/talks
Abstract:
AI for weather and climate has arrived, but new scientific insight has lagged behind. To get the most out of these tools, we need to stop treating them solely as faster forward simulators and start taking advantage of what AI architectures can uniquely do, such as running extreme events "on rewind" to directly sample antecedent conditions. Furthermore, the era of "bigger is better" is ending, and the field is starting to reward smart over big. This makes explaining model decision-making even more critical. While forecasts tell us what a model can do, and post-hoc explainable AI tells us where it looks, training tells us how it thinks. We don't have to open the black box after the fact; we can watch it being built. In this talk, I will show how creative task design and investigations into training dynamics allow us to both uncover new climate science and improve current state-of-the-art approaches.
Bio: Dr. Elizabeth Barnes is the Dalton Family Chair in Environmental Data Science & Sustainability, Professor of Computing & Data Sciences, and Professor of Earth & Environment at Boston University, which she joined in 2025.
Her group's research focuses on understanding Earth system variability, predictability, and change across time and space, with an emphasis on developing and implementing artificial intelligence tools in a way that reflects scientific reasoning to improve intrinsic interpretability. An overarching research goal of her group is to responsibly harness AI to anticipate human-Earth system futures in support of a thriving society in the decades ahead.
Bio:
Dr. Elizabeth Barnes is the Dalton Family Chair in Environmental Data Science & Sustainability, Professor of Computing & Data Sciences, and Professor of Earth & Environment at Boston University, which she joined in 2025.
Her group's research focuses on understanding Earth system variability, predictability, and change across time and space, with an emphasis on developing and implementing artificial intelligence tools in a way that reflects scientific reasoning to improve intrinsic interpretability. An overarching research goal of her group is to responsibly harness AI to anticipate human-Earth system futures in support of a thriving society in the decades ahead.