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  <NewsItem contentIssues="false" id="150391" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/150391">
    <Title>ScienceNews Article featuring iHARP Research</Title>
    <Tagline>Fires in the Amazon forest may melt sea ice in Antarctica</Tagline>
    <Body>
      <![CDATA[
          <div class="html-content"><span><span>ScienceNews article on Fires in the Amazon forest may melt sea ice in Antarctica features Dr.<a href="https://www.linkedin.com/in/ACoAAAMpbQMBCTl5v8RzXXzch-rOXIQcy9PIRjE" rel="nofollow external" class="bo"></a><a href="https://www.linkedin.com/in/sudip-chakraborty-28392215/" rel="nofollow external" class="bo">SUDIP CHAKRABORTY</a></span>and team's research on black carbon impact on ice melt in the Antarctica.<span><br></span><span><br></span></span><div><span>Congratulations to Dr. Chakraborty and team for having their iHARP research highlighted in ScienceNews.</span></div><br><div><br></div><div>Click here to read the full article <a href="https://www.sciencenews.org/article/antarctica-sea-ice-loss-amazon-fires">https://www.sciencenews.org/article/antarctica-sea-ice-loss-amazon-fires</a>.</div><div><br></div><div><br></div><div>Photo Information and Credit: <br></div><div><span>Antarctica’s Weddell Sea 
          (pictured) saw particularly large sea ice loss between 2018 and 2019 — 
          loss that may be linked to increased soot in the atmosphere from fires 
          in the Amazon. by </span><span>John Sonntag/NASA</span><span></span></div></div>
      ]]>
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    <Summary>ScienceNews article on Fires in the Amazon forest may melt sea ice in Antarctica features Dr.SUDIP CHAKRABORTYand team's research on black carbon impact on ice melt in the Antarctica....</Summary>
    <Website>http://iharp.umbc.edu</Website>
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  <NewsItem contentIssues="false" id="150390" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/150390">
  <Title>iHARP Senior Researcher Dr. Mohamed Mokbel receives prestigious Award</Title>
  <Tagline>2025 IEEE ICDE 10-Year Influential Paper Award</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div><span><span>iHARP's Dr.<a href="https://www.linkedin.com/in/ACoAAAZ8OHgBb9tUevCYTlDcx1WEsp8kFcL5iDw" rel="nofollow external" class="bo"></a><a href="https://www.linkedin.com/in/mohamed-mokbel-4a053130/" rel="nofollow external" class="bo">Mohamed Mokbel</a></span> (UMN),
     and his former student, Ahmed Eldawy, recently received the prestigious
     award 2025 IEEE ICDE 10-Year Influential Paper Award with for our work 
    on Big Spatial Data, where they have built the SpatialHadoop open-source
     system.</span></div><div><span><br></span></div><div><span><span>In that work, they have 
    built and released the SpatialHadoop open-source system, which significantly impacted the way that big spatial data is supported in 
    open-source and commercial systems. SpatialHadoop has been heavily used 
    by domain scientists worldwide and is a predecessor of many other 
    systems.</span></span><br></div><div><span></span></div><span><span><br></span>Please join iHARP in congratulating Dr.<a href="https://www.linkedin.com/in/ACoAAAZ8OHgBb9tUevCYTlDcx1WEsp8kFcL5iDw" rel="nofollow external" class="bo"></a><a href="https://www.linkedin.com/in/mohamed-mokbel-4a053130/" rel="nofollow external" class="bo">Mohamed Mokbel</a></span><span> (UMN) </span>on this amazing achievement!</div>
]]>
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  <Summary>iHARP's Dr.Mohamed Mokbel (UMN),  and his former student, Ahmed Eldawy, recently received the prestigious  award 2025 IEEE ICDE 10-Year Influential Paper Award with for our work  on Big Spatial...</Summary>
  <Website>http://iharp.umbc.edu</Website>
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  <PostedAt>Fri, 23 May 2025 10:57:59 -0400</PostedAt>
  <EditAt>Tue, 27 May 2025 09:01:59 -0400</EditAt>
</NewsItem>
  <NewsItem contentIssues="false" id="149693" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/149693">
  <Title>Chhaya Kulkarni Successfully Defends Her Dissertation!</Title>
  <Tagline>Congratulations Dr. Chhaya Kulkarni!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><p><span>Please join us in congratulating Chhaya on her successful dissertation defense. Chhaya successfully defended her PhD dissertation on Thursday, April 24, 2025. </span></p><br><p><span>Chhaya has been an invaluable member of the iHARP and UMBC community. Starting Fall 2025, Chhaya will be joining the Department of Computer and Information Sciences at Towson University as an Assistant Professor. We look forward to seeing Chhaya grow in her new role as a faculty member  and continue her passion for deep and meaningful research. </span></p><br><p><span>Congratulations to Chhaya on all of her hard work!!! iHARP wishes her great success in her next adventure!</span></p><br><p><span>-------------------------------------------------</span></p><br><p><span>Dissertation Title: </span><span>Multi-Contextual Learning in Spatiotemporal Neighborhoods</span></p><br><p><span>Committee:</span></p><ul><li><p><span>Dr. Vandana Janeja (UMBC), Chair</span></p></li><li><p><span>Dr. Jianwu Wang (UMBC)</span></p></li><li><p><span>Dr. Karuna Joshi (UMBC)</span></p></li><li><p><span>Dr. Bayu Adhi Tama (UMBC/iHARP)</span></p></li><li><p><span>Dr. Nicole Schlegel (NOAA)</span></p></li></ul><br><p><span>Abstract:</span></p><p><span>This dissertation presents a multi-contextual learning framework within spatiotemporal neighborhoods to tackle analytical challenges in Earth observation data. The rapid rise in the amount of environmental data —as demonstrated by the European Space Agency's Copernicus program expanding from 2 to 20 petabytes per year and NASA's Earth Observing System producing 16 terabytes per day—poses difficulties to conventional analytical methods for managing multi-source data fusion, cross-scale pattern recognition, and spatial autocorrelation.</span></p><br><p><span>The work develops a context-aware spatiotemporal data analysis approach with a neighborhood-based spatiotemporal framework at its foundation. The framework employs Voronoi tessellation for micro-neighborhood generation and attribute-based grouping for macro-neighborhood generation. By incorporating contextual information from spatial proximity and attribute similarity, the approach captures nuanced patterns that traditional methods tend to ignore.</span></p><br><p><span>This multi-contextual learning framework is validated through two complementary application domains that serve as case studies. The Greenland Ice Sheet case demonstrates how the application of neighborhood analysis successfully encapsulates intricate melt behavior, accounting for local variability and interactions between temperature, albedo, and other variables. Digital twin simulations form a second test case by demonstrating that the same neighborhood-based approach is capable of delineating areas with analogous variance structures within high-resolution atmospheric data.</span></p><br><p><span>The methodological contributions of the dissertation are: (1) multi-contextual learning for spatiotemporal neighborhood formation; (2) Graph Deviation Networks for multivariate anomaly detection in such neighborhoods; (3) a comparative framework for the validity verification of data sources; and (4) spatial clustering for variance analysis. Each component tackles intrinsic challenges in spatiotemporal data analysis while offering practical solutions for environmental monitoring application scenarios. Results indicate that multi-contextual learning in spatiotemporal neighborhoods substantially enhances detection and interpretation capability for complex Earth observation data, with immediate implications for environmental monitoring, modeling, and satellite-based observational systems.ralize for multiple downstream tasks and can be extended to other domains beyond Earth Science.</span></p><br><br></div>
]]>
  </Body>
  <Summary>Please join us in congratulating Chhaya on her successful dissertation defense. Chhaya successfully defended her PhD dissertation on Thursday, April 24, 2025.    Chhaya has been an invaluable...</Summary>
  <Website>http://iharp.umbc.edu</Website>
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  <PostedAt>Thu, 01 May 2025 15:51:43 -0400</PostedAt>
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  <NewsItem contentIssues="true" id="149202" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/149202">
  <Title>Chhaya Kulkarni PhD Dissertation Defense</Title>
  <Tagline>Thursdasy,  April 24, 2025 at 10a [virtual]</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div>We are excited to share that <span><strong>Chhaya Kulkarni (UMBC)</strong> will be<strong> defending her PhD Dissertation on Thursday, April 24, 2025 at 10a (est)</strong></span></div><div><strong><br></strong></div><div><br><div><div><span><strong>Date:</strong> Thursday, April 24, 2025<br></span></div><div><span><strong>Time: </strong>10a (est)<br></span></div><div><span><strong>Webex Virtual Link:</strong> </span><a href="https://umbc.webex.com/meet/ckulkar1&amp;sa=D&amp;source=calendar&amp;ust=1744638730592909&amp;usg=AOvVaw0sVCREU9XCLE2gMztA2ojD" rel="nofollow external" class="bo">https://umbc.webex.com/meet/ckulkar1&amp;sa=D&amp;source=calendar&amp;ust=1744638730592909&amp;usg=AOvVaw0sVCREU9XCLE2gMztA2ojD</a></div><div><span><br></span></div><div><span><strong>Title</strong> </span>Multi-Contextual Learning in Spatiotemporal Neighborhoods</div><div><strong><br></strong></div><div><strong>Committee</strong></div><div><div>Dr. Vandana Janeja, Chair/Advisor</div><div>Dr. Jianwu Wang</div><div>Dr. Karuna Joshi</div><div>Dr. Bayu Adhi Tama</div><div>Dr. Nicole Schlegel</div></div><div><span><strong><br></strong></span></div><div><span><strong>Abstract</strong></span><br>This dissertation presents a multi-contextual learning framework within spatiotemporal neighborhoods to tackle analytical challenges in Earth observation data. The rapid rise in the amount of environmental data —as demonstrated by the European Space Agency's Copernicus program expanding from 2 to 20 petabytes per year and NASA's Earth Observing System producing 16 terabytes per day—poses difficulties to conventional analytical methods for managing multi-source data fusion, cross-scale pattern recognition, and spatial autocorrelation.<br><br>The work develops a context-aware spatiotemporal data analysis approach with a neighborhood-based spatiotemporal framework at its foundation. The framework employs Voronoi tessellation for micro-neighborhood generation and attribute-based grouping for macro-neighborhood generation. By incorporating contextual information from spatial proximity and attribute similarity, the approach captures nuanced patterns that traditional methods tend to ignore.<br><br>This multi-contextual learning framework is validated through two complementary application domains that serve as case studies. The Greenland Ice Sheet case demonstrates how the application of neighborhood analysis successfully encapsulates intricate melt behavior, accounting for local variability and interactions between temperature, albedo, and other variables. Digital twin simulations form a second test case by demonstrating that the same neighborhood-based approach is capable of delineating areas with analogous variance structures within high-resolution atmospheric data.<br><br>The methodological contributions of the dissertation are: (1) multi-contextual learning for spatiotemporal neighborhood formation; (2) Graph Deviation Networks for multivariate anomaly detection in such neighborhoods; (3) a comparative framework for the validity verification of data sources; and (4) spatial clustering for variance analysis. Each component tackles intrinsic challenges in spatiotemporal data analysis while offering practical solutions for environmental monitoring application scenarios. Results indicate that multi-contextual learning in spatiotemporal neighborhoods substantially enhances detection and interpretation capability for complex Earth observation data, with immediate implications for environmental monitoring, modeling, and satellite-based observational systems.<br></div></div></div></div>
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  <Summary>We are excited to share that Chhaya Kulkarni (UMBC) will be defending her PhD Dissertation on Thursday, April 24, 2025 at 10a (est)        Date: Thursday, April 24, 2025   Time: 10a (est)   Webex...</Summary>
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  <NewsItem contentIssues="true" id="148706" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/148706">
  <Title>iHARP Research Assistants Secure Amazing Internships for Summer 2025!</Title>
  <Tagline>Join us in celebrating their major accomplishment!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><span>Exciting news from iHARP! Three of our talented Research Assistants have secured incredible internships for Summer 2025.</span><br><ul><li><p><a href="https://www.linkedin.com/in/yuchuan-huang-db?trk=public_post-text" rel="nofollow external" class="bo"><span>Yuchuan Huang</span></a><span>,
     UMN iHARP PhD Student, will join Databricks in Mountain View, 
    California, to work on the Open Source Apache Spark project, focusing on
     developing new Spark SQL features and enhancing Spark performance and 
    user experience.</span><a href="https://www.linkedin.com/in/subhankargsh?trk=public_post-text" rel="nofollow external" class="bo"><span> </span></a></p></li><li><p><a href="https://www.linkedin.com/in/subhankargsh?trk=public_post-text" rel="nofollow external" class="bo"><span>Subhankar Ghosh</span></a><span>,
     UMN iHARP PhD Student, will join the Climate Science Institute at Oak 
    Ridge National Lab in Tennessee to work on Diffusion Models for climate 
    downscaling tasks as part of ORBIT: Oak Ridge Base Foundation Model for 
    Earth System Predictability.</span><a href="https://www.linkedin.com/in/tolulope-ale-6001?trk=public_post-text" rel="nofollow external" class="bo"><span> </span></a></p></li><li><p><a href="https://www.linkedin.com/in/tolulope-ale-6001?trk=public_post-text" rel="nofollow external" class="bo"><span>Tolulope Ale</span></a><span>,
     UMBC iHARP PhD Candidate, will join Microsoft Corporation to work on 
    machine learning and statistical modeling techniques for handling 
    large-scale data, contributing to innovative solutions in computer 
    hardware and software. </span></p></li></ul><br><p><span>Congratulations to all three! We can’t wait to see you grow and the impact you’ll make.</span></p></div>
]]>
  </Body>
  <Summary>Exciting news from iHARP! Three of our talented Research Assistants have secured incredible internships for Summer 2025.    Yuchuan Huang,  UMN iHARP PhD Student, will join Databricks in Mountain...</Summary>
  <Website>http://iharp.umbc.edu</Website>
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  <NewsItem contentIssues="true" id="147305" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/147305">
    <Title>SCIPE Newsletter</Title>
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    <Summary></Summary>
    <Website>https://scipe.umces.edu/</Website>
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    <PostedAt>Thu, 13 Feb 2025 09:51:33 -0500</PostedAt>
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  <NewsItem contentIssues="true" id="146810" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/146810">
  <Title>Francis Nji successfully defends his PhD Proposal</Title>
  <Tagline>Congratulatoins Francis!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div>Francis Nji, iHARP Research Assistant successfully defended his PhD Proposal on Monday, January 27, 2025. Join <a href="https://www.linkedin.com/company/80301570/admin/page-posts/published/?share=true#" rel="nofollow external" class="bo">iHARP</a> in congratulating Francis on his successful PhD Proposal defense! </div><div><br></div><div><strong>Title</strong></div><div><div>Accurate Clustering of Multi-dimensional Multivariate Spatiotemporal data</div></div><div><strong><br></strong></div><div><strong>Committee</strong></div><ul><li>Dr Jianwu Wang  - Advisor and Committee Chair (UMBC/ iHARP)</li><li>Dr Vandana Janeja - Co-advisor and Committee Member (UMBC/ iHARP)</li><li>Dr Aneesh Subramanian - Committee Member (UC-Boulder / iHARP)</li><li>Dr James Foulds - Committee Member (UMBC)</li><li>Dr Yiqun Xie - Committee Member (UMD)</li></ul><div><strong>Abstract</strong><br></div>The growing availability of multivariate spatiotemporal data, which 
    includes datasets containing both spatial and temporal dimensions across
     multiple variables, presents significant opportunities for extracting 
    insights into complex environmental systems, societal trends, and 
    dynamic processes in fields such as environmental monitoring, urban 
    planning, traffic management, transportation, social media analysis, 
    epidemiology, climatology, crime analysis and disaster management where 
    understanding the interactions between spatial locations and their 
    evolution over time is crucial for decision-making. Proper analysis of 
    these datasets enable researchers to understand interactions and 
    patterns that evolve over time and space, facilitating advancements in 
    predictive modeling, causal analysis, and decision-making for addressing
     global challenges like climate change, resource management, and public 
    health crises. One of such analytical approaches to extract meaningful 
    insights from this data is clustering. Clustering is the process of 
    grouping data with similar spatial attributes, temporal attributes, or 
    both, from which many significant events and regular phenomena can be 
    discovered. However, clustering this data is highly challenging due to 
    the complexities involved in accounting for both spatial autocorrelation
     and temporal dependencies, as well as the high dimensionality of 
    multivariate data. To tackle these challenges, this dissertation 
    presents three innovative approaches aimed at accurately partitioning 
    complex multivariate spatiotemporal data such that similar points are 
    grouped together and dissimilar points are segregated. Each proposed 
    model is designed to capture the nuanced spatial and temporal 
    relationships inherent in the data, while enhancing clustering 
    performance and stability. By leveraging advanced traditional and deep 
    learning techniques, the proposed models provide robust solutions for 
    managing the complexities of spatiotemporal datasets, resulting in more 
    accurate, stable and interpretable clustering outcomes.<br><br>The first
     proposed model, Hybrid Ensemble Deep Graph Temporal Clustering 
    (HEDGTC), integrates homogeneous and heterogeneous ensemble clustering 
    techniques in an attempt to harness their individual strengths while 
    mitigating their weaknesses. HEDGTC further employs a dual-consensus 
    approach to address noise and misclassification that might result from 
    base clusters. To obtain the desired clusters, HEDGTC employs a deep 
    graph attention autoencoder network which simultaneously updates the 
    clustering loss and reconstruction loss to improve the clustering 
    results in terms of performance and stability. When compared with 
    existing state-of-the-art ensemble models, HEDGTC outperforms with 
    significant margins proving capable to capture implicit temporal 
    patterns and provides consistent results when tested on real-world 
    multivariate spatiotemporal datasets. Although HEDGTC outperforms 
    existing ensemble algorithms, it has its limitations. Real-world 
    multivariate spatiotemporal data is truly complex and can be 
    characterized by non-linearity: variables may exhibit nonlinear 
    interdependencies, localized patterns: clusters may form in specific 
    regions of space, time, or feature combinations, irrelevant dimensions: 
    datasets often contain redundant information, or irrelevant variables 
    and overlapping clusters: a single data point can belong to different 
    clusters. In such cases, HEDGTC might have a hard time to deal with this
     dependencies therefore the need to develop advanced algorithms that, 
    unlike HEDGTC which rely on global features to perform clustering, will 
    include local feature subspaces and the capability to capture underlying
     structures in data with both spatial and temporal dimensions.<br><br>To
     address the limitations of HEDGTC, we propose a novel Attention-Guided 
    Deep Temporal Subspace Clustering (A-DATSC) for multivariate 
    spatiotemporal data. A-DATSC incorporates a deep subspace clustering 
    generator and a quality-verifying discriminator that work in tandem. 
    Inspired by the recent success of the U-Net architecture, the generator 
    combines CNN-RNN-attention mechanisms in an autoencoder to capture 
    spatial, temporal and salient representations respectively present in 
    multivariate spatiotemporal data. The autoencoder is equipped with a 
    fully connected GNN-based self-expressive network that extracts the 
    weights of the latent features into a coefficient matrix and a 
    clustering layer that performs clustering through the optimization of 
    the reconstruction loss, self-expressive loss and clustering loss in a 
    iterative manner. The discriminator evaluates current clustering 
    performance by inspecting whether the re-sampled data from estimated 
    subspaces have consistent subspace properties, and supervises the 
    generator to progressively improve subspace clustering. Experimental 
    results on three real-world multivariate spatiotemporal data demonstrate
     the advantages of A-DATSC over shallow and few deep subspace clustering
     models. <br><br>In recent years, research on clustering analysis has 
    largely focused on improving accuracy and efficiency, often at the cost 
    of interpretability. Geospatial clustering of multivariate 
    spatiotemporal data plays a critical role in analyzing complex spatial 
    patterns for applications such as urban planning, mobility analysis, and
     climate monitoring. However, the interpretability of clustering results
     remains a significant challenge due to the "black-box" nature of 
    clustering algorithms and the inherent complexity of multivariate 
    spatiotemporal data. Ensuring interpretability is essential for 
    fostering trust, meeting ethical standards, and complying with 
    regulatory requirements, as clustering-derived decisions must be 
    transparent and justifiable. To address these challenges, we propose a 
    novel end-to-end Interpretable Causal Clustering (ICC) model for high 
    dimensional multivariate spatiotemporal data. ICC employs a 
    causal-discovery feature engineering pre-clustering and a causal 
    inference in-clustering phase. Pre-clustering is achieved through an 
    ensemble of causal discovery methods to prioritize causally significant 
    features, enhanced by spatial modeling and sparsity regularization to 
    focus on relevant features. In-clustering is achieved through a U-Net 
    Autoencoder architecture with stacked GATv2 layers for capturing spatial
     dependencies and ConvLSTM for temporal modeling. ICC integrates a 
    Probabilistic Discriminative Model (PDM) at the latent encoding layer to
     further enhance the encoding of causally significant features, ensuring
     that the latent representations respect causal constraints. ICC 
    incorporates Dynamic Bayesian Networks as a causal inference technique 
    to ensure that the clustering process respects causal dependencies. To 
    improve clustering results, ICC introduces a causal regularization loss 
    term that penalizes clusters that violate causal constraints. To further
     enhance interpretability, ICC introduces Counterfactual reasoning that 
    seeks to validate clusters for causal consistency and maps them onto 
    geospatial and temporal causal graphs. This further tests the validity 
    of the clusters if they reflect true causal relationships. ICC mitigates
     confounding effects by explicitly modeling confounders which reduces 
    noise and spurious correlations. Experimental results demonstrate that 
    ICC significantly enhances interpretability and accuracy in geospatial 
    clustering, offering actionable insights into the dynamics of 
    multivariate spatiotemporal climate data. We plan to evaluate our 
    approach on a suite of synthetic and real world clustering problems, and
     compare across state of the art interpretable and non-interpretable 
    clustering algorithms.<br></div>
]]>
  </Body>
  <Summary>Francis Nji, iHARP Research Assistant successfully defended his PhD Proposal on Monday, January 27, 2025. Join iHARP in congratulating Francis on his successful PhD Proposal defense!      Title...</Summary>
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  <NewsItem contentIssues="true" id="146779" important="true" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/146779">
    <Title>Full-Circle Climate Communication Workshop</Title>
    <Body>
      <![CDATA[
          <div class="html-content"><p><span>Thank you to those who attended and participated in the first</span><a href="https://irc.umbc.edu/full-circle-climate-communication-workshop/" rel="nofollow external" class="bo"><span> Full-Circle Climate Communication Workshop </span></a><span>held at UMBC between January 13 to 17, 2025. The workshop successfully brought together over 50 people from a wide range of disciplines and with varied expertise to consider how best to communicate issues regarding climate resilience. </span></p><br><p><span>During the workshop, participants were challenged to strategize and develop new and innovative ways to communicate in a transdisciplinary team. Each team consisted of a variety of experts from a wide range of fields that included but were not limited to Public Policy, Data Science, Artificial Intelligence, Data Visualization, Visual Arts, Performance Arts, Geography, Storytelling, Media Communication, and Behavioral Sciences. </span></p><br><p><span>We truly thank everyone who made the workshop a success. </span></p><br><p><span>Full-Circle Climate Workshop Committee:  Dr. Lee Boot, Dr. Anita Komlodi, Dr. Eric Stokan, Dr. Rebecca Williams, Dr. Josephine Namayanja, Nikki Monczewski</span></p><br><p><span>Those who helped support, and we could not have done it without them, include: Jasmin Diaz, Xiaofei Xie, and Hemanth Chelluri, as well as graduate student volunteers from iHARP.</span></p><br><p><span>This workshop was organized by the </span><a href="https://iharp.umbc.edu/" rel="nofollow external" class="bo"><span>NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP)</span></a><span> in collaboration with the </span><a href="https://irc.umbc.edu/" rel="nofollow external" class="bo"><span>Imaging Research Center (IRC)</span></a><span>, and the </span><a href="https://socialscience.umbc.edu/" rel="nofollow external" class="bo"><span>Center for Social Science Scholarship (CS3</span></a><span>)., .  </span></p><br><p><span>If you are interested in joining, participating, or speaking at the next</span><a href="https://forms.gle/mgVQk9fEfPMdVCHg7" rel="nofollow external" class="bo"><span> Full-Circle Communication Workshop, please click here to join our mailing list</span></a><span>. </span></p></div>
      ]]>
    </Body>
    <Summary>Thank you to those who attended and participated in the first Full-Circle Climate Communication Workshop held at UMBC between January 13 to 17, 2025. The workshop successfully brought together...</Summary>
    <Website>http://iharp.umbc.edu</Website>
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    <PostedAt>Mon, 27 Jan 2025 16:28:48 -0500</PostedAt>
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  <NewsItem contentIssues="true" id="146771" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/146771">
  <Title>Rohan Putatunda successfully defends his PhD Proposal</Title>
  <Tagline>Congratulatoins Rohan!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div>Rohan Putatunda, iHARP Research Assistant successfully defended his PhD Proposal on Monday, January 27, 2025. Join <a href="https://www.linkedin.com/company/80301570/admin/page-posts/published/?share=true#" rel="nofollow external" class="bo">iHARP</a> in congratulating Rohan on his successful PhD Proposal defense! </div><div><br></div><div><strong>Title</strong></div><div><div>Deep Learning for Ice Calving Front Analysis: Advancing Understanding through AI-Driven Techniques</div></div><div><strong><br></strong></div><div><strong>Committee</strong></div><ul><li>Dr. Vandana P. Janeja, (Chair)</li><li>Dr. Sanjay Purushotham, (Co-Chair)</li><li>Dr. Rebecca Williams</li><li>Dr. Zhiyuan Chen</li><li>Dr. SUDIP CHAKRABORTY<br></li></ul><div><strong>Abstract</strong><br></div>Ice calving, the process where large ice masses detach from a glacier’s 
    terminus, is a significant driver of ice mass loss and contributes to 
    global sea-level rise. In ice calving, ice calving fronts refer to the 
    terminus regions where these events occur, often producing icebergs as 
    large as Manhattan, which eventually fragment into smaller pieces, or 
    "chicklets." These chicklets, influenced by environmental forces such as
     ocean currents and winds, follow complex trajectories that can disrupt 
    maritime routes. Although traditional methods for monitoring ice-calving
     fronts rely heavily on manual reviews of time series satellite imagery,
     they are time-intensive, prone to human error, and lack scalability. 
    Recent advances in deep learning have introduced automation in 
    segmenting ice-calving fronts, but critical challenges such as 
    predicting future calving front positions and forecasting chicklet 
    trajectories remain underexplored. This thesis addresses three 
    fundamental challenges in understanding and predicting ice-calving 
    processes. First, it tackles the challenge of accurate segmentation of 
    ice calving fronts, characterized by sparse pixel representation, 
    through SEATTNET, a novel hybrid attention model that combines 
    squeeze-and-excitation (SE) blocks with spatial attention gates to 
    enhance feature representation and segmentation accuracy. Second, it 
    explores forecasting future ice-calving front position, where the 
    absence of explicit spatiotemporal data and the nonlinear nature of 
    latitude-longitude temporal sequences present significant challenges. To
     address these, a georeferenced dataset derived from segmentation masks 
    is utilized alongside the GlaSpectra model, which employs spectral 
    convolution layers along with FFT and IFFT to capture global and local 
    spatial relationships, enabling precise trajectory forecasting. Finally,
     this thesis also proposes to tackle the challenge of predicting the 
    movement of calved ice fragments, known as icebergs or "chicklets," 
    whose trajectories are highly unpredictable due to the influence of 
    ocean currents, winds, and other environmental factors. To address this,
     we used spatiotemporal data of iceberg movement over time and applied 
    ConvLSTM layers to capture both the spatial patterns and temporal 
    dynamics. To make predictions more accurate, we developed a custom drift
     loss function that focuses on two key aspects: how the size and shape 
    of the chicklets change over time and how their movement patterns 
    evolve. The loss function incorporates the principle that the larger the
     volume of the ice mass, the slower its movement pattern is likely to 
    be, ensuring that the model captures this critical physical 
    relationship. This approach enables our proposed deep learning based 
    model to not only predict the trajectory of the icebergs but also 
    understand the relationship between their motion and their changing 
    dimensions. By addressing these complexities, this research not only 
    advances the use of AI in glaciology, offering practical, and scalable, 
    tools for studying and predicting ice calving and its after-effect of 
    ice chicklets behavior, but also has potential applications in studying 
    landslide movements, and coastal erosion movement.<div><em><br></em></div></div>
]]>
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  <Summary>Rohan Putatunda, iHARP Research Assistant successfully defended his PhD Proposal on Monday, January 27, 2025. Join iHARP in congratulating Rohan on his successful PhD Proposal defense!      Title...</Summary>
  <Website>https://iharp.umbc.edu/</Website>
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  <PostedAt>Mon, 27 Jan 2025 15:50:11 -0500</PostedAt>
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  <NewsItem contentIssues="true" id="146207" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/146207">
  <Title>Akila Sampath successfully defends her PhD Proposal</Title>
  <Tagline>Congratulatoins Akila!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div>Akila Sampath, iHARP Research Assistant, successfully defended her PhD 
    Proposal on Wednesday, December 4, 2024. Join iHARP in congratulating Akila on her successful PhD proposal defense. <br></div><div><br></div><div><strong>Title</strong></div><div><div>Leveraging Physical Principles in Deep Learning to Study Arctic Sea Ice</div></div><div><strong><br></strong></div><div><strong>Committee</strong></div><ul><li>Dr. Jianwu Wang, Chair/Advisor (UMBC)</li><li>Dr. Vandana Janeja, Co-Chair (UMBC)</li><li>Dr. Houbing Song (UMBC)</li><li>Dr. James Foulds  (UMBC)</li><li>Dr. Donald.k.Perovich (Dartmouth College)</li><li>Dr. Nicole Schlegel (NOAA)</li></ul><div><strong>Abstract</strong><br></div><div>The Arctic region is undergoing rapid environmental changes, with sea 
    ice loss being a prominent indicator. Accurate prediction of sea ice 
    thickness and extent is crucial for understanding climate change impacts
     and informing policy decisions. Traditional climate models often 
    struggle to capture the complex interactions between atmospheric, 
    oceanic, and sea ice processes. In recent years, machine learning has 
    emerged as a powerful tool for analyzing large datasets and making 
    accurate predictions. However, machine learning models, while capable of
     capturing complex patterns, can sometimes produce unrealistic or 
    physically implausible results. To address this limitation, we propose a
     novel approach that combines the power of machine learning with the 
    rigor of physical principles. By integrating physics-informed techniques
     into machine learning models, we aim to develop more accurate and 
    reliable predictions of sea ice conditions.</div><div><br></div><div>Physics-Informed Machine 
    Learning (PIML) is a novel approach that blends traditional scientific 
    models with the pattern-recognition capabilities of machine learning to 
    improve predictions of sea ice thickness. By leveraging both data-driven
     and physics-based knowledge, PIML offers more accurate and reliable 
    predictions, even in data-scarce environments. This approach is 
    particularly well-suited for studying complex systems like the Arctic 
    sea ice. Our research introduces Physics-Encoded Neural Networks (PeNNs)
     to predict snow density. PeNNs embed physical laws directly into the 
    neural network architecture, enabling the estimation of hidden physical 
    properties from observable data. This makes PeNNs highly effective for 
    tracking snow and ice conditions with precision. Additionally, we 
    developed a physics-guided model (PGM) to investigate the causal link 
    between sea ice loss and increased turbulence in the Beaufort Gyre 
    through wind current. By incorporating physical constraints, the PGM 
    ensures that predictions align with known scientific principles, aiding 
    in the understanding of these causal relationships. Our proposed models 
    offer a more comprehensive approach to studying Arctic sea ice. By 
    integrating physics into machine learning, we aim to create tools that 
    are both accurate and interpretable. This research provides valuable 
    insights into Arctic climate change and demonstrates how physics can 
    enhance the capabilities of machine learning models, leading to a deeper
     understanding of the impacts of sea ice loss.</div><div><em><br></em></div><div><em>At the top pictured is Akila Sampath, <br></em></div><em>Middle Row, left to right:  Dr. Jianwu Wang,  Dr. Vandana Janeja, Dr.Nicole Schlegel. <br>Bottom Row, left to right: Dr. Donald.k.Perovich, Dr. James Foulds,  Dr. Dr. Houbing Song</em><br></div>
]]>
  </Body>
  <Summary>Akila Sampath, iHARP Research Assistant, successfully defended her PhD  Proposal on Wednesday, December 4, 2024. Join iHARP in congratulating Akila on her successful PhD proposal defense....</Summary>
  <Website>https://iharp.umbc.edu/</Website>
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