3 citations · 4 across the 6 of their papers we have counts for
6 papers
Scalable Changepoint Detection for Large Spatiotemporal Data on the Sphere
Samantha Shi-Jun, Bo Li
We propose a novel Bayesian framework for changepoint detection in large-scale spherical spatiotemporal data, with broad applicability in environmental and climate sciences. Our ap…
MAT-MPNN: A Mobility-Aware Transformer-MPNN Model for Dynamic Spatiotemporal Prediction of HIV Diagnoses in California, Florida, and New England
Zhaoxuan Wang, Weichen Kang, Yutian Han +2
Human Immunodeficiency Virus (HIV) has posed a major global health challenge for decades, and forecasting HIV diagnoses continues to be a critical area of research. However, captur…
Spatially Varying Deep Functional Neural Network: Application in Large-Scale Crop Yield Prediction
Yeonjoo Park, Bo Li, Yehua Li
Accurate prediction of crop yield is critical for supporting food security, agricultural planning, and economic decision-making. However, yield forecasting remains a significant ch…
Tracing the impacts of Mount Pinatubo eruption on regional climate using spatially-varying changepoint detection
Samantha Shi-Jun, Lyndsay Shand, Bo Li
Significant events, such as volcanic eruptions, can have global and long-lasting impacts on climate. These global impacts, however, are not uniform across space and time. Understan…
Combining Climate Models using Bayesian Regression Trees and Random Paths
John C. Yannotty, Thomas J. Santner, Bo Li +1
General circulation models (GCMs) are essential tools for climate studies. Such climate models may have varying accuracy across the input domain, but no model is uniformly best. On…
Validating Climate Models with Spherical Convolutional Wasserstein Distance
Robert C. Garrett, Trevor Harris, Bo Li +1
The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more compre…