Event Details

DSNE seminar with Claire Monteleoni, University of Colorado Boulder, USA

Despite the scientific consensus on climate change,drastic uncertainties remain. Crucial questions about regional climate trends,changes in extreme events, such as heat waves and mega-storms, and understanding how climate varied in the distant past, must be answered in order to improve predictions, assess impacts and vulnerability, and inform mitigation and sustainable adaptation strategies. Machine learning can help answer such questions and shed light on climate change. I will give an overview of our climate informatics research, focusing on challenges in learning from spatiotemporal data and climate model projections, along with semi- and unsupervised deep learning approaches to studying rare and extreme events, and downscaling temperature and precipitation.

Bio:

Claire Monteleoni is an Associate Professor of ComputerScience at the University of Colorado Boulder, which she joined in 2018,following positions at University of Paris-Saclay, CNRS, George WashingtonUniversity, and Columbia University. She completed her PhD and Masters inComputer Science at MIT and was a postdoc at UC San Diego. She holds aBachelor’s in Earth and Planetary Sciences from Harvard. Her research onmachine learning for the study of climate change helped launch theinterdisciplinary field of Climate Informatics. In 2011, she co-founded theInternational Conference on Climate Informatics, which turns 10 years old in2020, and has attracted climate scientists and data scientists from over 20countries and 30 U.S. states. She gave an invited tutorial: Climate Change:Challenges for Machine Learning, at NeurIPS 2014.

Please join viaZoom using this link:

https://us02web.zoom.us/j/85434124002?pwd=SS8xcEd3bXBPT1kwYTFaV0lBYUxvUT09

MeetingID: 854 3412 4002

Password:3E9qdP