4 papers
OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning
Kevin Valencia, Thilina Balasooriya, Xihaier Luo +2
Multimodal spatiotemporal learning on real-world experimental data is constrained by two challenges: within-modality measurements are sparse, irregular, and noisy (QA/QC artifacts)…
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Miruna Oprescu, David K. Park, Xihaier Luo +2
Estimating causal effects from spatiotemporal observational data is essential in public health, environmental science, and policy evaluation, where randomized experiments are often…
STACI: Spatio-Temporal Aleatoric Conformal Inference
Brandon R. Feng, David Keetae Park, Xihaier Luo +3
Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while s…
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
David Keetae Park, Xihaier Luo, Guang Zhao +3
Spatiotemporal learning is challenging due to the intricate interplay between spatial and temporal dependencies, the high dimensionality of the data, and scalability constraints. T…