2 citations · 4 across the 8 of their papers we have counts for
9 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…
Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
Francis Ndikum Nji, Omar Faruque, Mostafa Cham +2
Classifying subsets based on spatial and temporal features is crucial to the analysis of spatiotemporal data given the inherent spatial and temporal variability. Since no single cl…
gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method
Seraj Al Mahmud Mostafa, Omar Faruque, Chenxi Wang +3
Atmospheric gravity waves occur in the Earths atmosphere caused by an interplay between gravity and buoyancy forces. These waves have profound impacts on various aspects of the atm…
Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference
Sahara Ali, Omar Faruque, Jianwu Wang
Spatial interference (SI) occurs when the treatment at one location affects the outcomes at other locations. Accounting for spatial interference in spatiotemporal settings poses fu…
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
Sahara Ali, Uzma Hasan, Xingyan Li +5
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science. More specifically, the paper presents a…
TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
Omar Faruque, Sahara Ali, Xue Zheng +1
The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for…