JSM2024
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Contributed Papers

Recent Advances in Machine Learning Based Methods for Spatio-Temporal Analysis

Thu, Aug 8, 10:30 AM - 12:20 PM
Seiyon LeeChair
Section on Statistics and the Environment

6 Presentations

Deep Granger Causal Effect Estimation in Spatiotemporal Processes
10:35 AM - 10:50 AM
Eric Gao (Texas A&M University)
Co-authors: Scott Bruce, Trevor Harris
Explaining pathways from stratospheric aerosol injections using spatio-temporal feature importance
10:50 AM - 11:05 AM
Daniel Ries (Sandia National Laboratories)
Co-authors: Katherine Goode (Sandia National Laboratories), Kellie McClernon (Sandia National Laboratories)
Spatial Deep Convolutional Neural Networks
11:05 AM - 11:20 AM
Qi Wang (University of California, Santa Cruz)
Co-authors: Paul Parker (University of California Santa Cruz)
Modeling high and low extremes with a novel dynamic spatio-temporal model
11:20 AM - 11:35 AM
Myungsoo Yoo
Co-authors: Likun Zhang (University of Missouri-Columbia), Christopher Wikle (University of Missouri-Columbia)
Modeling Spatio-temporal Extremes with Conditional Variational Autoencoders
11:35 AM - 11:50 AM
Xiaoyu Ma (University of Missouri)
Co-authors: Likun Zhang (University of Missouri-Columbia), Christopher Wikle (University of Missouri-Columbia)
Predicting Bat Roost Locations using Bayesian Additive Regression Trees
11:50 AM - 12:05 PM
Jacob Oram (Montana State University)
Co-authors: Patrick Pollock (Montana State University), Andrew Hoegh (Montana State University)
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