1 citations · 1 across the 3 of their papers we have counts for
5 papers
Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift
Emam Hossain, Muhammad Hasan Ferdous, Devon Dunmire +2
Causal modeling offers a principled foundation for uncovering stable, invariant relationships in time-series data, thereby improving robustness and generalization under distributio…
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…
Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction
Emam Hossain, Muhammad Hasan Ferdous, Jianwu Wang +2
Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, whi…
Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling
Subhankar Ghosh, Arun Sharma, Jayant Gupta +2
Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data, capturing fine-s…
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach
Subhankar Ghosh, Shuai An, Arun Sharma +3
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important becau…