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Machine Learning for Earth System Modeling |
Kara Lamb (she/her) is an Associate Research Scientist at Columbia University in the NSF Learning the Earth with Artificial Intelligence and Physics (LEAP) Center and collaborates with researchers at NASA GISS on the NASA Digital Twins for Climate Science Project. Her research lies at the intersection of observations (from laboratory and field studies) and high-resolution modeling, with the goal of improving climate model parameterizations of aerosol and cloud processes. She combines traditional process-based approaches with data science, scientific machine learning, and reduced-order modeling. She was on the science team for the NASA KORUS-AQ and AToM aircraft campaigns and the NOAA FIREX Firelab study, was the lead mentor for the 2022 Frontier Development Laboratory Europe challenge on Aerosols, and is a member of the AMS STAC Committee on AI Applications to Environmental Science.
Julia Kaltenborn (she/her) is a PhD student at McGill University and the Mila-Quebec AI Institute. She explores machine learning for climate model emulation and cryospheric sciences. She focuses on creating ML resources that can be used cross-disciplinarily, such as ClimateSet, and developing ML models that are actively used in the field, e.g., by MOSAiC, the largest polar expedition in history. Julia co-founded the NGO Unser Dialog, co-organized AI Helps Ukraine, and was a local Greenpeace leader in her youth. She has been a fellow of the German Academic Scholarship Foundation, received the Mitacs Globalink Research Fellowship, and was a DeepMind scholar. Last but not least, Julia was a member of the Juneau Icefield Research Expedition 2023 and has traversed the Juneau Icefield.
David Rolnick (he/him) is an Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at Mila Quebec AI Institute, where his work focuses on applications of machine learning to help address climate change. He is a Co-founder and Chair of Climate Change AI and Scientific Co-director of Sustainability in the Digital Age. Dr. Rolnick received his Ph.D. in Applied Mathematics from MIT. He is a former NSF Mathematical Sciences Postdoctoral Research Fellow, NSF Graduate Research Fellow, and Fulbright Scholar, and was named to the MIT Technology Review’s 2021 list of “35 Innovators Under 35.
Aditi Krishnapriyan (she/her) is an assistant professor of Chemical Engineering and EECS at UC Berkeley where she is also a member of Berkeley AI Research (BAIR) and part of the AI+Science group in EECS and the theory group in Chemical Engineering. She is interested in developing methods in machine learning that are driven by the distinct challenges and opportunities in the natural sciences, with particular interest in physics-inspired machine learning methods. Some areas of exploration include approaches to incorporate physical inductive biases (such as symmetries, conservation laws) into ML models to improve generalization for scientific problems, the advantages that ML can bring to classical physics-based numerical solvers (such as through end-to-end differentiable frameworks and implicit layers), and better learning strategies for distribution shifts in the physical sciences.
Nils Thuerey (he/him) is an associate professor at the computer science department of the Technical University of Munich (TUM) where he leads a research group working on deep learning methods for physical simulations, with an emphasis on fluid flow prob- lems. He has co-authored a freely available, Jupyter based textbook on physics-based deep learning, and his group has pioneered novel methods for improving the stability, rollout, and physical consistency of neural network-based PDE solvers. He is especially interested in solvers that employ traditional numerics alongside learned components, and, more recently, in leveraging diffusion modelling for improved neural simulations.
Stephan Mandt (he/him) is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. From 2016 until 2018, he was a Senior Researcher and Head of the statistical machine learning group at Disney Research in Pittsburgh and Los Angeles. He held previous postdoctoral positions at Columbia University and Princeton University. Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne, where he received the German National Merit Scholarship. He is furthermore a recipient of the NSF CAREER Award, the UCI ICS Mid-Career Excellence in Research Award, the German Research Foundation’s Mercator Fellowship, a Kavli Fellow of the U.S. National Academy of Sciences, a member of the ELLIS Society, and a former visiting researcher at Google Brain. Stephan is an Action Editor of the Journal of Machine Learning Research and Transaction on Machine Learning Research and currently serves as Program Chair for AIS- TATS 2024.