Tags: iHarp

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  1. Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice

    16 Jul 2024 | Contributor(s):: Emam Hossain, Sahara Ali, Yiyi Huang, Nicole Schlegel, Jianwu Wang, Aneesh Subramanian, Md Osman Gani

    Abstract: Arctic sea ice (ASI) is playing a pivotal role in keeping global warming under control. However, the recently amplified decreasing sea ice trend has become a major concern. Since satellites started monitoring the ASI in 1979, every decade the Arctic has lost 13.1% of sea ice and the...

  2. A Survey on Causal Discovery Methods for IID and Time Series Data

    16 Jul 2024 | Contributor(s):: Uzma Hasan, Emam Hossain, Md Osman Gani

    Abstract: 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 relationships among the variables of a system from related observational data with certain assumptions. Over the...

  3. Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography

    15 May 2024 | Contributor(s):: Homayra Alam, Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Sikan Li, Mathieu Morlighem, Omar Faruque, Jianwu Wang

    Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...

  4. REU_Final_Presentation

    15 May 2024 | Contributor(s):: Homayra Alam, Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Omar Faruque, Sikan Li, Mathieu Morlighem

    Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...

  5. Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography

    15 May 2024 | Contributor(s):: Homayra Alam, Jianwu Wang, Tartela Tabassum, Katherine Yi, Angelina Dewar, Jason Lu, Ray Chen, Omar Faruque, Mathieu Morlighem, Sikan LI

    Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...