PLEASE SELECT ONLY ONE TICKET TYPE. All Columbia Affiliates are encouraged to attend in person.

ABOUT THE LECTURE

Over the last couple of years, we have witnessed an explosion in the use of machine learning for Earth system science application 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 for specific examples and some of the needed advances in machine learning to push climate science forward.

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.

IN-PERSON ATTENDEES: Please note that all non-Columbia Affiliates must show proof of vaccination prior to entering the building, per the University's COVID Safety guidelines.

Columbia Engineering reserves the right to capture and use images (including video, photo, audio) of student participants at this event in its current or future marketing materials. These materials include but are not limited to: social media, digital and/or print posters, email and web-based materials. By attending and participating in this event, you are consenting to having your image captured for these purposes. If you have concerns about your likeness being used, please reach out to engineeringcommunications@columbia.edu and we will accommodate your request.

Physics to Machine Learning and Machine Learning Back to Physics