11 citations · 11 across the 20 of their papers we have counts for
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Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
Bayu Adhi Tama, Jianwu Wang, Vandana Janeja +1
Accurate subglacial bed topography is essential for ice sheet modeling, yet radar observations are sparse and uneven. We propose a physics-guided residual learning framework that p…
Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data
Francis Ndikum Nji, Vandana Janeja, Jianwu Wang
Deep subspace clustering models are vital for applications such as snowmelt detection, sea ice tracking, crop health monitoring, infectious disease modeling, network load predictio…
B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data
Francis Ndikum Nji, Vandana Janaja, Jianwu Wang
Clustering high-dimensional multivariate spatiotemporal climate data is challenging due to complex temporal dependencies, evolving spatial interactions, and non-stationary dynamics…
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…
Modeling Heterogeneity across Varying Spatial Extents: Discovering Linkages between Sea Ice Retreat and Ice Shelve Melt in the Antarctic
Maloy Kumar Devnath, Sudip Chakraborty, Vandana P. Janeja
Spatial phenomena often exhibit heterogeneity across spatial extents and in proximity, making them complex to model-especially in dynamic regions like ice shelves and sea ice. In t…
IDRIFTNET: Physics-Driven Spatiotemporal Deep Learning for Iceberg Drift Forecasting
Rohan Putatunda, Sanjay Purushotham, Ratnaksha Lele +1
Drifting icebergs in the polar oceans play a key role in the Earth's climate system, impacting freshwater fluxes into the ocean and regional ecosystems while also posing a challeng…
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…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
Tolulope Ale, Nicole-Jeanne Schlegel, Vandana P. Janeja
The focus of our work is improving the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics. The Arctic and Antarctic ice sheets…