4 papers
Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference
Omar Faruque, Pavan Raj Ravi, Jianwu Wang
Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference. Standard causal identification methods assume conditional e…
TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
Omar Faruque, Sahara Ali, Xue Zheng +1
The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust causal discovery methods that c…
Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data
Bayu Adhi Tama, Homayra Alam, Mostafa Cham +3
Accurate maps of Greenland's subglacial bed are essential for sea-level projections, but radar observations are sparse and uneven. We introduce GraphTopoNet, a graph-learning frame…
DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets
Bayu Adhi Tama, Mansa Krishna, Homayra Alam +6
Understanding Greenland's subglacial topography is critical for projecting the future mass loss of the ice sheet and its contribution to global sea-level rise. However, the complex…