Seminars and Panels

Spring 2022

Seminar: Machine Learning Rain to Improve Weather and Climate Prediction
Speaker: Dr. Andrew Gettelman, National Center for Atmospheric Research (NCAR)
Date: Wednesday, April 27, 2022
Time: 4:30pm – 5:30pm EST
Format:Hybrid (in-person and virtual options)
In-person location: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 206, New York, NY 10027 (Directions)
Virtual option: Zoom link provided upon registration

Abstract: Rain is a critical part of the hydrologic cycle and a key process in understanding the evolution of climate. This talk will provide a background on why and how rain is a critical part of the climate system. The methods for representing rain formation at the scale of micrometer (10-6m) in models which represent the earth’s general circulation (at the scale of 10+6m) are approximate, and this is a critical problem in weather and climate. To approach this problem, we take detailed numerical approaches to the rain process typically used for small scale models, and put them in a global model. This results in simulations that have higher fidelity with respect to observations and reduce longstanding biases in the global model, but at prohibitive computational cost. We then train neural networks on the detailed treatments, and put those back into the full model. This reproduces all the major features of the detailed simulations from weather to climate, with no impact in computational cost. Diagnostics on the neural networks illustrate the challenges in representing these processes with machine learning such as the potential for overfitting and problems with extrapolation, as well as how physical processes important for climate challenge machine learning methods and provide opportunities for extending them.

Bio: Dr. Andrew Gettelman is a senior scientist at the National Center for Atmospheric Research, specializing in global climate modeling. His work concerns development and analysis of global model simulations of the climate system with a focus on development of physical parameterizations of clouds in global climate models, and the physics and chemistry of the Upper Troposphere and Lower Stratosphere. In addition to developing and managing community earth system models at NCAR, Dr. Gettelman is involved in model evaluation data analysis with satellites and field programs, having served on several satellite science teams and on the science teams for field projects. Dr. Gettelman has been a visiting professor in Physics in Oxford, an Erskine visiting fellow at the University of Canterbury in Christchurch, New Zealand, and a visiting professor in the Institute for Atmospheric and Climate Science at the Swiss Federal Institute of Technology (ETH) in Zürich, Switzerland. Dr. Gettelman is a member of the atmospheric model development team for the Community Earth System Model. Dr. Gettelman is a contributing author and reviewer for international scientific assessments of climate change and ozone depletion. He has served on the COMET advisory board, and the NCAR Earth Observing Laboratory advisory board. Dr. Gettelman is author or co-author on over 200 peer reviewed publications, and a textbook (Demystifying Climate Models, Springer). Dr. Gettelman has a PhD in Atmospheric Sciences from the University of Washington and a Bachelors of Science in Civil Engineering from Princeton University.

Panel: LEAP, Learning the Earth with Artificial Intelligence and Physics (video recording)

LEAP Team Presenters: Courtney D. Cogburn, Pierre Gentine, and Tian Zheng, Columbia; Dave Lawrence, National Center for Atmospheric Research (NCAR)
Date: Tuesday, April 26, 2022
Time: 2:00pm – 3:00pm ET
Format: Hybrid (in-person and virtual options)
In-person location: Columbia Business School, Kravis (Room 690), 665 W 130th Street, New York, NY 10027
Virtual option: Zoom link provided upon registration 

Please join the Computational Social Science (CSS) Working Group for a special event with LEAP, Learning the Earth with Artificial Intelligence and Physics, the recently launched NSF STC here at Columbia University. We will discuss the core areas of LEAP’s research, why education, equity, and bi-directional knowledge transfer are core to LEAP’s mission, why climate modeling may be important for your scholarship, and the open-access data and computing platform, LEAPangeo, that we hope will transform climate data science. In addition to introducing LEAP to the broader Columbia community, our discussion will also set a foundation for LEAP engagement, collaboration, and data and knowledge sharing generally.

For speaker bios and more information, please view the event listing, courtesy of Columbia’s Data Science Institute.

Seminar: From probabilistic forecasting to neural data compression and back: a latent variable perspective.

Speaker: Stephan Mandt, UC Irvine
Date: Monday, March 28, 2022
Time: 4:10pm – 5:10pm ET
Format: Hybrid (in-person and virtual options)
In-person location: School of Social Work, 1255 Amsterdam Avenue, Room 903 (Directions)
Virtual option: Zoom Link (passcode 860249)

Abstract: The past few years have seen deep generative models mature into promising applications. Two of these applications include neural data compression and forecasting high-dimensional time series, including video. I will begin by reviewing the basic ideas behind neural data compression and show how advances in approximate Bayesian inference and generative modeling can significantly improve the compression performance of existing models. Finally, I show how neural video codecs can inspire probabilistic forecasting, leading to probabilistic sequence prediction methods with high potential for data-driven weather prediction.

Bio: Stephan Mandt is an Assistant 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, first in Pittsburgh and later in 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. Furthermore, he is a Kavli Fellow of the U.S. National Academy of Sciences, an NSF CAREER Awardee, a member of the ELLIS Society, and a former visiting researcher at Google Brain. Stephan regularly serves as an Area Chair, Action Editor, or Editorial Board member for NeurIPS, ICML, AAAI, ICLR, TMLR, and JMLR. His research is currently supported by NSF, DARPA, DOE, Disney, Intel, and Qualcomm.

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