In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 202, New York, NY 10027
*Please note that in-person space is limited.*
Please register here: https://www.eventbrite.com/e/physics-to-machine-learning-and-machine-learning-back-to-physics-tickets-523020607717
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Abstract: Over the last couple of years, we have witnessed an explosion in the use of machine learning for Earth system science applications ranging from Earth monitoring to modeling. Machine learning has shown tremendous success in emulating complex physics such as atmospheric convection or terrestrial carbon and water fluxes using satellite or high-fidelity simulations in a supervised framework. However, machine learning, especially deep learning, is opaque (the so-called black box issue) and thus a question remains: what (new) understanding have we really developed?
I will here illustrate the value of machine learning to understand or discover new processes in climate, with an application to rainfall organization. I will also present new tools merging causal discovery and machine learning to improve the trustworthiness and interpretability of machine learning for climate and physics applications.
Bio: Pierre Gentine is the Maurice Ewing and J. Lamar Worzel professor of geophysics in the departments of Earth and Environmental Engineering and Earth and Environmental Sciences at Columbia University. He studies the terrestrial water and carbon cycles and their changes with climate change. Pierre Gentine is recipient of the National Science Foundation (NSF), NASA and Department of energy (DOE) early career awards, as well as the American Geophysical Union Global Environmental Changes Early Career, Macelwane medal and American Meteorological Society Meisinger award. He is the director of the new NSF Science and Technology Center (STC) for Learning the Earth with Artificial intelligence and Physics (LEAP), the largest funding mechanism of the NSF.
