18 citations · 33 across the 14 of their papers we have counts for
18 papers
Supraglacial Lake Fate Is Knowable Long Before the Season Ends
Emam Hossain, Md Osman Gani
A supraglacial lake on the Greenland Ice Sheet ends its melt season in one of four ways: it drains rapidly through a hydrofracture, drains slowly across the surface, refreezes in p…
DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
Muhammad Hasan Ferdous, Ian Stockwell, Md Osman Gani
Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating ca…
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…
Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Prediction
Emam Hossain, Md Osman Gani
Conventional machine learning and deep learning models typically rely on correlation-based learning, which often fails to distinguish genuine causal relationships from spurious ass…
TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery
Muhammad Hasan Ferdous, Emam Hossain, Md Osman Gani
Robust causal discovery in time series datasets depends on reliable benchmark datasets with known ground-truth causal relationships. However, such datasets remain scarce, and exist…
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification
Siyuan Wang, James R. Foulds, Md Osman Gani +1
In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corrobo…
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…
Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet
Emam Hossain, Md Osman Gani, Devon Dunmire +2
The Greenland Ice Sheet (GrIS) has emerged as a significant contributor to global sea level rise, primarily due to increased meltwater runoff. Supraglacial lakes, which form on the…
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…
Optimizing Data-driven Causal Discovery Using Knowledge-guided Search
Uzma Hasan, Md Osman Gani
Learning causal relationships solely from observational data often fails to reveal the underlying causal mechanisms due to the vast search space of possible causal graphs, which ca…
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations. In this s…
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
Uzma Hasan, Emam Hossain, Md Osman Gani
The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationshi…
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
Sahara Ali, Omar Faruque, Yiyi Huang +4
The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are st…
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget
Sourajit Saha, Shaswati Saha, Md Osman Gani +2
Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN)architectures with downstream and upstream propagation flow are p…
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
Time series data are found in many areas of healthcare such as medical time series, electronic health records (EHR), measurements of vitals, and wearable devices. Causal discovery,…
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset
Riddhiman Adib, Md Osman Gani, Sheikh Iqbal Ahamed +1
Delirium occurs in about 80% cases in the Intensive Care Unit (ICU) and is associated with a longer hospital stay, increased mortality and other related issues. Delirium does not h…
CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors
Riddhiman Adib, Md Mobasshir Arshed Naved, Chih-Hao Fang +5
Structural causal models (SCMs) provide a principled approach to identifying causation from observational and experimental data in disciplines ranging from economics to medicine. H…
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU
Md Osman Gani, Shravan Kethireddy, Marvi Bikak +2
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observ…